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[post_date] => 2019-02-28 19:55:38
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[post_content] => Many organizations have started utilizing DevOps practices and tools for data warehousing and data lake setups. Data Analysts and Database Managers can follow DevOps practices for managing updates and new database releases across various environments in a uniform fashion, to produce repeatable results. Just like application teams create and manage the CI/CD pipeline for applications, the data that these applications consume can have its own release pipeline that is managed by the database teams. In many cases, cloud based data warehousing platforms provide the ability to host the applications that consume this data, all within the same environment. Applications that consume data housed in a data warehouse may also leverage Kafka or other DevOps tools to achieve low latency query performance. As you release updates to your applications, you may also need to account for the updates to the service bus layer and the database layer. Continuous deployment and continuous integration becomes all the more important. Data teams that institute DevOps practices and tools for data warehousing can promote an agile culture within their silos. This includes the process of fetching or discovering the data for data warehousing, the process of making sure it is current and accurate for the consuming applications, and the process of organizing it for data mining and analysis. You can apply DevOps practices and policies to data automation (just like infrastructure automation). Starting from self-service models to request new data instances, to requesting updates, and other data lifecycle steps. There are many organizations that have built entire data platforms on containers. For infrastructure and database teams, it is imperative to provide data "as-a-service" with measured and tracked SLAs and costs – whether these services are provided on container platforms or otherwise. Public cloud platforms have made it easy for consumers to leverage SaaS data warehousing solutions. Using DevOps practices do not have to be limited to providing the underlying infrastructure or service, but can also be applied to the building of reports. Jenkins automation can be used to release database updates, integration tools can be used to fetch the relevant data from multiple sources to populate the target systems, and opensource tools like Grafana can be used for dashboards. Primary objective of such a setup would be to capture data from various components and locations within the environment to a centralized location via ETL, and process that data to produce business intelligence. When bringing data in from multiple sources for data warehousing, the exercise of data mapping and data reconciliation and sanitization usually take the most time and effort upfront. Architectural considerations also include the paradigm of monitoring the data warehouse components, as well as the data within it. Data processing engines like Hadoop MapReduce or Spark, along with the database serving platforms form the core components of any data warehouse setup. By implementing the best practices architecture, and tuning specifically for your environment, you can optimize your data warehouse setup to achieve a balance between performance and cost. Various industry use cases like fraud prevention in banking, storing health records and doctors notes in healthcare, customer profiling for retail, real time streaming in media, and others, have already leveraged the benefits provided by data lakes for capturing and storing unstructured data, and data warehousing for structured data. With the adoption of blockchain technologies, the relevance of Big Data is only anticipated to grow. Most enterprises depend heavily on applications for their business, and thereby have adopted agile processes for application releases. Combining the consumption of Big Data with emphasis on extracting relevant and accurate data at the right time, is paramount for business critical applications. The adoption of DevOps practices and tools for data warehousing within data teams is still in its nascent stage, but is being picked up by more and more data experts every day. If you need assistance with data warehousing to move your disparate data from various sources, or need help assessing the feasibility of a data warehouse platform without substantially affecting your business critical applications, Keyva can help. Associates at Keyva have worked with many different organizations in various verticals to help in data migration and application modernization projects. These include things like creating a data migration factory, creating ETL strategies with data mapping, refactoring existing applications, adding a wrapper over current applications so they can be consumed easily by DevOps processes, modifying existing applications to consume data from SaaS platforms, and more. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at info@keyvatech.com.
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => DevOps and Data Warehousing
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[post_content] => This write-up walks through setting up a two node Hadoop v3.1.1 cluster, and running a couple of sample MapReduce jobs. Prerequisites:Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => How to set up Hadoop two node cluster and run MapReduce jobs
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[post_content] => This technical guide will walk you through the installations of Ansible v2.4.2.0, an open source configuration management and deployment tool, and Ansible Tower (web layer for Ansible) v3.4.1 on a RHEL 7 virtual machine. Ansible Tower is a RedHat supported and paid version of AWX, which is open source. We will first enable the required repos:
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => Installation Guide: Ansible & Ansible Tower - Day 1
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[post_content] => You may have heard the term Microservices when talking about applications, and some cases even when talking about underlying infrastructure. If you've worked with any OOPS language, you must be familiar with the concept of abstraction and modularization. Extend that idea to an application - the breakdown of independent functions abstracted in to separate services; with a bunch of services working together forming an application. Typically, the more fine grained and decoupled your services are, the more scalability can be achieved. Let's take a look at some of the reasons why it is important for your critical applications to be be implemented with a microservices architecture framework -
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => What is Microservices Architecture?
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[post_content] => If you've tried to install Kong from scratch, you know that it is a little more involved than what is posted on the official website guide. There are some pre-requisites that the system needs to be set up with, in order to make sure the service installs and runs successfully. Let us take a look at the steps involved in setting up a Kong instance from the ground up. This guide is valid for RHEL 7, PostgreSQL 11, and Kong 0.14.1. We will set up Kong on a VMware virtual machine. If you are using another hypervisor, the steps for hardware configurations may differ slightly. First, let us take a look at the pre-requisite steps that need to be addressed before we begin our work.
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => Installing Kong: A First Day Guide
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[post_date] => 2019-01-17 20:45:17
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[post_content] => We've all heard that containers and cloud technologies help us avoid lock-ins. While most of that is true, you could be unintentionally locking yourself to a particular technology or framework, if you are not careful. Let's talk about some important aspects to consider, when you are evaluating a cloud or a container platform. 1. Compile a list the features you want a platform to have, based on your requirements Your needs to consume a hybrid cloud architecture may be different from other organizations. The vision of having more flexibility and agility may be the same, but your workloads are different. It is important to know the in-depth architecture of the applications and workloads you want to live in the cloud or container environment. 2. Support You may have thousands of commercial-off-the-shelf applications, and hundreds of custom built ones. Some application vendors may not support containerizing their applications. There could be regulatory or other limitations that prevent certain legacy applications from being supported on containers. Depending upon how critical and integral such applications are to your business, you may want to consider newer offerings that make the full use of cloud and containerization benefits like microservices architecture. 3. Licensing and Exit costs Platform cost is always a factor, no matter the size of the organization. One of the native benefits that containerization provides, is the ability to control costs – assuming your applications are architected accordingly. For example, a cloud-native application can be scaled in to public cloud for just the webtier when necessary (assuming microservices architecture) rather than spinning up large capacity VMs on demand in the cloud for monolithic applications. Another important consideration is around how the platforms are priced. A platform that prices based on worker nodes may be more feasible for an organization that plans on developing hundreds or thousands of microservices, and making them run efficiently on worker nodes. A platform priced based on the number of applications containerized may be more feasible for monolithic applications. You will need to take in to account how your organization plans to scale the containerizing of applications, and what the application architectures will be like. Although you will be making a decision based on long term usage of the platform, it is always a good idea to see what the exit costs will be once you decide to move away from the chosen platform. As an exercise, see if you can figure out what changes would need to be made to the application architecture, the deployment architecture, resource skill sets, and more, if you were to move away from the chosen platform. That can then serve as a yardstick for how locked-in you might be, upon choosing a particular solution. There will always be some level of customization effort an lock-in, no matter which solution you choose – but the idea would be to minimize the monetary and non-monetary exit costs. 4. Detailed architecture knowledge about your applications Nobody wants to make major technology adoption decisions based on hearsay. It is critical to know the detailed application architectures, as that will guide you towards choosing the right solution. If the bulk of your application workloads are legacy monolithic applications, the primary tangible benefit obtained by containerizing them will be horizontal scaling. This is assuming the application vendors will support the containerized versions, the applications will allow multiple instances running within the same sub-net, the application can be used with a load balancer, and so on. To leverage the self-healing and other native capabilities offered by containers, the cloud-native applications developed in a microservices architecture would be an ideal workload. But in the real-world, there is always going to be a mix of legacy and newer workloads. For highly regulated industries like healthcare, insurance, banking, and others – legacy applications may form the biggest chunk of their workloads. If you have the time, it would be ideal to do a cost-benefit analysis for legacy workloads to compare the benefits offered by horizontal scalability and the like, before choosing to move forward with containerization. 5. Implementing vendor agnostic tools, and processes, refactoring if necessary Choosing a platform is just one piece of this complex puzzle involving the rollout of new technologies. There are critical tasks pre- and post- containerization, that also need to be accounted for. For example, how easy or difficult will it be to refactor the existing application for a chosen platform? How easy or difficult will it be to monitor the performance of the chosen platform? How easy or difficult will it be to integrate the chosen platform with other toolsets? Will there be changes needed to your existing processes or toolsets, so they can work effectively with the chosen platform? The end goal for most organizations is to have an easy transition path into Cloud and Container platforms – and to use these platforms as effective tools for their IT teams and business in general. This vision can be best achieved by having a vendor agnostic strategy, and avoiding vendor lock-ins wherever possible. Associates at Keyva have helped multiple organizations assess their application readiness and move them in to containerized and cloud environments. Keyva also helps with the pre- and post- containerization steps. These include things like refactoring existing applications, adding a wrapper over current applications so they can be consumed easily by DevOps processes, and more. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at info@keyvatech.com
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => Avoid Cloud & Container Vendor Lock-In
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[post_content] => You might've heard this before – configuration management database (CMDB) should be the single source of truth. But what does that mean? And how can you achieve it? With all the different third party applications in your environment, you might think it could be a gargantuan effort to consolidate all the data in to the CMDB. It may be a decent amount of work, but the reality is that it is easier than what most would anticipate. By taking the 3 step approach below, you can come close to configuration management database nirvana – a current and accurate CMDB: Step 1: Develop, and agree on a configuration management database schema, and the necessary mappings This step requires the IT Service Management (ITSM) teams, and the various business units to agree on a configuration management database schema and how it will be organized. ITSM teams would create data mappings – to help map data captured from various software components in the environment, into the specified fields within the CMDB schema. This would also include activities around customization of various fields within the CMDB forms, and customizations around API access. Step 2: Integrate and Automate Integrate the configuration management database with all sources of data as per the identified data mappings. This can be done by leveraging existing integrations, or by creating new ones. The population of data within the CMDB can be done as part of an extract-transfer-load process (retrospectively), or as part of the creation of a CI using automation (prospectively). The process of CMDB population is a multi-step process, whereby data is captured via one of the discovery tools and automatically updated within the CMDB. Automation of CI population also helps create relationships between CIs and Change tickets or Incident tickets, thereby making the review process for Change Advisory Boards much easier. Step 3: Optimize and Reconcile Once the data is in the configuration management database, it is important to make sure it is accurate. Given that many different sources of data may compete for the same target field within the CMDB, weights can be assigned to each source to improve accuracy. For example, the asset tag of a device may have a higher weightage when that data is coming from a discovery tool, but the CPU information captured within a configuration management system can be trusted more than any other source. Furthermore, the data captured from all the various sources should be put in staging datasets. It is up to the administrators of the system, to define rule sets and reconciliation rules that will automatically filter the required data in to production data set for consumption. The above may seem like oversimplification of the tasks required to have a fully functional CMDB, but many organizations have successfully adopted a version of this breakdown. It is highly likely that the most time will be spent upfront during the configuration management database schema and data mapping exercise. By investing time and effort towards having an accurate CMDB, organizations can effectively understand the various configurations and their relationships within in the environment, and thereby easily track and manage them. Associates at Keyva have been helping customers set up and optimize their ITSM and CMDB systems for the past two decades. We've also helped organizations develop integrations between configuration management database and third party application software so as to accelerate the population of the CMDB, and to keep it current and accurate. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at info@keyvatech.com
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => 3 Steps to Achieving Configuration Management Database (CMDB) Nirvana
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[post_date] => 2019-01-11 16:54:38
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[post_content] => If you have tried setting up a DevOps pipeline to achieve continuous deployment, and ran into configuration drift issues - you will know the pains quite well. A consistent and uniform configuration is a prerequisite to any automation. If the automated workflow finds that the target configuration is not as anticipated, it will either take the exception route, or revert to notification for manual intervention. What is configuration drift? When a given system configuration or an application configuration changes from the "blessed" or "vetted" state, to another state, it is called configuration drift. For example, if the IT team provides web server instance with a preset configuration file, and as part of the application deployment process or application customization process, that configuration is changed – that constitutes a configuration drift. Similar drifts can occur at the OS level, for packages or commercial software. Why does it matter? It is important to mitigate and remediate any configuration drifts because without it, the environment will become unmanageable, especially as you scale. Consider the web server example above, when there is an application outage in your production environment. As part of finding the root cause of failure, you will now also need to walk back the steps of every configuration changed from the original version, to rule out any issues caused because of those changes. This can cause time and efforts to be deployed towards tangential activities. With consistent deployment automation, you can confidently evaluate the issues plaguing the core application, given all other things to be constant. Another reason that reducing or preventing configuration drift is paramount, is to make sure that any additional deployments on the base tier can be automated. In the above example, it is much easier to automate application deployment on a web server delivered as PaaS, than deploying the same application on a web server that might've been customized, or drifted from its desired state. How to mitigate or prevent configuration drift? There are many ways you can address the resolution of configuration drift. The common factor for all scenarios is to make sure the deployments are automated. Firstly, you'd have to define what constitutes a configuration drift. For example, if you are providing your customers with IaaS machines, would adding a new printer constitute configuration drift? Or would that be classified under allowed customizations, that have no material effect on your service delivery? The rules of configuration will need to be defined. Secondly, you'd want to automate the deployments of your builds and configurations. This could mean using an orchestration framework to deploy the desired service through a self-service catalog, or another automated mechanism. Thirdly, you'd want to make sure that the release and update process for various infrastructure and application components flows through a source control management (SCM) system. Any and all deployments should pick up the latest version of the configs from SCM. And by deploying a systems configuration management solution, you can now check the configuration states of your target systems against the latest versions of those configurations in SCM. There are many other steps you can take to mitigate configuration drift, depending upon the severity of the drift and the penalty you pay for not addressing it. Keyva has helped several organizations address the common challenge of preventing configuration drift. There are several processes and tools an organization can use to address their customized needs for preventing configuration drifts in their infrastructure and applications. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at info@keyvatech.com
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => Configuration Drift : The Bane of Continuous Deployments
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[post_content] => Many organizations have started utilizing DevOps practices and tools for data warehousing and data lake setups. Data Analysts and Database Managers can follow DevOps practices for managing updates and new database releases across various environments in a uniform fashion, to produce repeatable results. Just like application teams create and manage the CI/CD pipeline for applications, the data that these applications consume can have its own release pipeline that is managed by the database teams. In many cases, cloud based data warehousing platforms provide the ability to host the applications that consume this data, all within the same environment. Applications that consume data housed in a data warehouse may also leverage Kafka or other DevOps tools to achieve low latency query performance. As you release updates to your applications, you may also need to account for the updates to the service bus layer and the database layer. Continuous deployment and continuous integration becomes all the more important. Data teams that institute DevOps practices and tools for data warehousing can promote an agile culture within their silos. This includes the process of fetching or discovering the data for data warehousing, the process of making sure it is current and accurate for the consuming applications, and the process of organizing it for data mining and analysis. You can apply DevOps practices and policies to data automation (just like infrastructure automation). Starting from self-service models to request new data instances, to requesting updates, and other data lifecycle steps. There are many organizations that have built entire data platforms on containers. For infrastructure and database teams, it is imperative to provide data "as-a-service" with measured and tracked SLAs and costs – whether these services are provided on container platforms or otherwise. Public cloud platforms have made it easy for consumers to leverage SaaS data warehousing solutions. Using DevOps practices do not have to be limited to providing the underlying infrastructure or service, but can also be applied to the building of reports. Jenkins automation can be used to release database updates, integration tools can be used to fetch the relevant data from multiple sources to populate the target systems, and opensource tools like Grafana can be used for dashboards. Primary objective of such a setup would be to capture data from various components and locations within the environment to a centralized location via ETL, and process that data to produce business intelligence. When bringing data in from multiple sources for data warehousing, the exercise of data mapping and data reconciliation and sanitization usually take the most time and effort upfront. Architectural considerations also include the paradigm of monitoring the data warehouse components, as well as the data within it. Data processing engines like Hadoop MapReduce or Spark, along with the database serving platforms form the core components of any data warehouse setup. By implementing the best practices architecture, and tuning specifically for your environment, you can optimize your data warehouse setup to achieve a balance between performance and cost. Various industry use cases like fraud prevention in banking, storing health records and doctors notes in healthcare, customer profiling for retail, real time streaming in media, and others, have already leveraged the benefits provided by data lakes for capturing and storing unstructured data, and data warehousing for structured data. With the adoption of blockchain technologies, the relevance of Big Data is only anticipated to grow. Most enterprises depend heavily on applications for their business, and thereby have adopted agile processes for application releases. Combining the consumption of Big Data with emphasis on extracting relevant and accurate data at the right time, is paramount for business critical applications. The adoption of DevOps practices and tools for data warehousing within data teams is still in its nascent stage, but is being picked up by more and more data experts every day. If you need assistance with data warehousing to move your disparate data from various sources, or need help assessing the feasibility of a data warehouse platform without substantially affecting your business critical applications, Keyva can help. Associates at Keyva have worked with many different organizations in various verticals to help in data migration and application modernization projects. These include things like creating a data migration factory, creating ETL strategies with data mapping, refactoring existing applications, adding a wrapper over current applications so they can be consumed easily by DevOps processes, modifying existing applications to consume data from SaaS platforms, and more. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at info@keyvatech.com.
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => DevOps and Data Warehousing
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Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => DevOps and Data Warehousing
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[post_content] => This write-up walks through setting up a two node Hadoop v3.1.1 cluster, and running a couple of sample MapReduce jobs. Prerequisites:- Two machines set up with RHEL 7. You could use another distribution, but the commands may vary.
- Perl, wget, and other required packages downloaded using yum
- Disable the firewall, or open up connectivity between the two machines. Since we are setting it up as a lab instance, we will go ahead and disable the firewall
systemctl stop firewalld systemctl disable firewalld
- hadoop1 will be the master node, and hadoop2 will be the datanode.
- Add entry for hadoop1 and hadoop2 under /etc/hosts on both machines. We will need a JDK installation (on both machines):
yum install java-1.8.0-openjdk -y
You can validate that java is installed by querying for the installed versionjava -version
Create a separate directory under '/' path where we will download the bits for hadoop (on both machines)mkdir hadoop cd /hadoop/ wget http://mirror.cc.columbia.edu/pub/software/apache/hadoop/common/hadoop-3.1.1/hadoop-3.1.1.tar.gz tar -xzf hadoop-3.1.1.tar.gz
In order to point hadoop to the correct java installation, we will need to capture the full path of java installreadlink -f $(which java)
Export the path as environment variable (on both machines)export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.191.b12-1.el7_6.x86_64/jre/
We will modify the bashrc profile file to make sure that all the required environment variables are available when we log in to the machine console. This change is made (on both machines):vi ~/.bashrc
Add the following lines to the file
export HDFS_NAMENODE_USER="root"
export HDFS_DATANODE_USER="root" export HDFS_SECONDARYNAMENODE_USER="root" export YARN_RESOURCEMANAGER_USER="root" export YARN_NODEMANAGER_USER="root" export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.191.b12-1.el7_6.x86_64/jre/ export PATH=$PATH:$JAVA_HOME/binUpdate the core-site file (on the master node) vi /hadoop/hadoop-3.1.1/etc/hadoop/core-site.xml Modify the <configuration> section as per below:
<configuration> <property> <name>fs.defaultFS</name> <value>hdfs://hadoop1:9000</value> </property> </configuration>Update the hdfs-site file (on the master node)
vi /hadoop/hadoop-3.1.1/etc/hadoop/hdfs-site.xmlModify the <configuration> section as per below:
<configuration> <property> <name>dfs.replication</name> <value>1</value> </property> </configuration>Set up the machines for passwordless SSH access (on both machines):
ssh-keygen ssh-copy-id -i ~/.ssh/id_rsa.pub root@hadoop1 ssh-copy-id -i ~/.ssh/id_rsa.pub root@hadoop2On the master node, update the workers file to reflect the slave nodes
vi /hadoop/hadoop-3.1.1/etc/hadoop/workersAdd the entry
hadoop2And then on the master node, format the hdfs file system:
/hadoop/hadoop-3.1.1/bin/hdfs namenode -formatOn the datanode, format the hdfs file system:
/hadoop/hadoop-3.1.1/bin/hdfs datanode –formatOn the master node, start the dfs service:
/hadoop/hadoop-3.1.1/sbin/start-dfs.shOn the master node, run the dfsadmin report, to validate the availability of datanodes
/hadoop/hadoop-3.1.1/bin/hdfs dfsadmin -reportThe output of this command should show two entries for datanodes - one for hadoop1 and one for hadoop2. The nodes are now set up to handle MapReduce jobs. We will look at two examples. We will use the sample jobs from hadoop-mapreduce-examples-3.1.1.jar file under the share folder. There is a large number of opensource java projects available, which run various kinds of mapreduce jobs. We will run these exercises on the master node. Exercise 1: We will solve a sudoku puzzle using MapReduce. First we will need to create a sudoku directory under root folder in hdfs file system.
/hadoop/hadoop-3.1.1/bin/hdfs dfs -mkdir /sudokuThen create an input file with the sudoku puzzle, under your current directory:
vi solve_this.txtUpdate the file with the below text. Each entry on the same line is separated by a space.
? 9 7 ? ? ? ? ? 5 ? 6 3 ? 4 ? 2 ? ? ? ? ? 9 ? ? ? 8 ? ? ? 9 ? ? ? ? 7 ? ? ? ? 1 ? 6 ? ? ? 2 5 4 8 3 ? ? ? 1 ? 7 ? ? ? 1 8 ? ? ? 8 ? ? 7 ? 6 ? 4 5 ? ? ? ? 2 ? 9 ?Now move (put) the file from your current directory in to the hdfs folder (sudoku) that we created earlier.
/hadoop/hadoop-3.1.1/bin/hdfs dfs -put solve_this.txt /sudoku/solve_this.txtTo make sure that the file was copied:
/hadoop/hadoop-3.1.1/bin/hdfs dfs -ls /sudokuRun the mapreduce job, to solve the puzzle:
/hadoop/hadoop-3.1.1/bin/hadoop jar /hadoop/hadoop-3.1.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.1.jar sudoku solve_this.txt Solving solve_this.txt 1 9 7 6 2 8 4 3 5 8 6 3 7 4 5 2 1 9 4 2 5 9 1 3 7 8 6 6 1 9 2 5 4 3 7 8 7 3 8 1 9 6 5 4 2 2 5 4 8 3 7 9 6 1 9 7 2 4 6 1 8 5 3 3 8 1 5 7 9 6 2 4 5 4 6 3 8 2 1 9 7Found 1 solutions Exercise 2: We will run a wordcount method on the sudoku puzzle file. Run the wordcount method on the sudoku puzzle file, and have the output stored in wcount_result folder.
/hadoop/hadoop-3.1.1/bin/hadoop jar /hadoop/hadoop-3.1.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.1.jar wordcount /sudoku/solve_this.txt /sudoku/wcount_resultThe lengthy output lists out the results of detailed analysis conducted on the file. We will cat the results of various results,
/hadoop/hadoop-3.1.1/bin/hdfs dfs -cat /sudoku/wcount_result/* 1 3 2 3 3 2 4 3 5 3 6 3 7 4 8 4 9 4 ? 52The above output captures the total number of times a particular digit is listed in the solved puzzle. To see all the different sample methods available under hadoop-mapreduce-examples-3.1.1.jar, run the following command:
/hadoop/hadoop-3.1.1/bin/hadoop jar /hadoop/hadoop-3.1.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.1.jarIf you have any questions about the steps documented here, would like more information on the installation procedure, or have any feedback or requests, please let us know at info@keyvatech.com.
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => How to set up Hadoop two node cluster and run MapReduce jobs
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[post_content] => This technical guide will walk you through the installations of Ansible v2.4.2.0, an open source configuration management and deployment tool, and Ansible Tower (web layer for Ansible) v3.4.1 on a RHEL 7 virtual machine. Ansible Tower is a RedHat supported and paid version of AWX, which is open source. We will first enable the required repos:sudo subscription-manager repos --enable rhel-7-server-ansible-2.6-rpms subscription-manager repos --enable rhel-7-desktop-optional-rpmsYou can install the latest version of ansible using Yum:
yum install ansible(Since we will be installing Ansible Tower on this same machine, it is recommended to use the Yum method to install Ansible). -OR- You can build the RPM package by downloading the latest version of Ansible code from Git. If choosing this method, first we will need to get all the pre-requisite libraries ready (some of these are optional):
yum update yum install python-dev python-pip wget yum install git yum update -y nss curl libcurl yum install rpm-build yum -y install pythonDownload the latest code, and build:
mkdir ansible cd ansible/ git clone https://github.com/ansible/ansible.git systemctl stop firewalld systemctl disable firewalld cd ./ansible/ make rpm rpm -Uvh ./rpm-build/ansible-*.noarch.rpmOnce installed, you can view and modify the default Ansible hosts file at /etc/ansible/hosts You can also verify successful installation using the command:
ansible –-versionNow, we can go ahead and set up Ansible Tower on this machine. We will be using the integrated installation, which installs the GUI, the REST API, and the database – all on the same machine:
mkdir ansible-tower cd ansible-tower/ wget https://releases.ansible.com/ansible-tower/setup-bundle/ansible-tower-setup-bundle-3.4.1-1.el7.tar.gz tar xvzf ansible-tower-setup-bundle-3.4.1-1.el7.tar.gz cd ansible-tower-setup-bundle-3.4.1-1.el7/Tower connects to the PostgreSQL database using password authentication. We will need to create a md5 hash to configure Tower to talk with the database. Replace <CUSTOM-DB-PASSWORD> with a password of your choosing:
python -c 'from hashlib import md5; print("md5" + md5("<CUSTOM-DB-PASSWORD>" + "awx").hexdigest())'Make a note of the hash key generated from this command. We will use it for our next step. We have to now update the inventory file (located within ansible-tower-setup-bundle-3.4.1-1.el7 directory) with the passwords for the database, the hash key generated above, and a custom password of our choosing for rabbit_mq. Find the following lines and update them accordingly. First, for setting the admin password for the consoleadmin_password='AdminPassword'Next, set the password for database connectivity. Please note, this password should be the same as what you used to replace <CUSTOM-DB-PASSWORD> during the hash key generation step above. Also, we will paste the copied hash key, and set it for the hashed password line
pg_password='password' pg_hashed_password='md5f58b4d5d85dbde46651335d78bb56b8c'And finally, choose a custom password for rabbit_mq
rabbitmq_password='password'We are now ready to run the setup script
./setup.shOnce all the steps are completed successfully, you can verify the Tower installation by going to the URL
https://<MACHINE-IP-OR-FQDN>:443You can use the admin credentials (username: admin, password: admin password as defined in the inventory file) to log in and access the console. You can request a free Ansible Tower license for an evaluation environment of up to 10 nodes, or can purchase a RedHat subscription for larger environments, and some additional logging, management and support features. If you have any questions about the steps documented here, would like more information on the installation procedure, or have any feedback or requests, please let us know at info@keyvatech.com.
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => Installation Guide: Ansible & Ansible Tower - Day 1
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[post_name] => installing-guide-ansible-and-ansible-tower-day-1
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[post_modified] => 2019-02-21 19:08:28
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[post_content] => You may have heard the term Microservices when talking about applications, and some cases even when talking about underlying infrastructure. If you've worked with any OOPS language, you must be familiar with the concept of abstraction and modularization. Extend that idea to an application - the breakdown of independent functions abstracted in to separate services; with a bunch of services working together forming an application. Typically, the more fine grained and decoupled your services are, the more scalability can be achieved. Let's take a look at some of the reasons why it is important for your critical applications to be be implemented with a microservices architecture framework -Scalability: By splitting down the function of an application in to a microservices, it can be scaled independently of other functions or components. For example, a microservice responsible for managing database connections can scale independently of the web tier, if the db connection pool size is to be increased.
Decoupling: Separating the functions into individual services provides flexibility for design, implementation, and maintenance of those individual services.
Continuous Delivery & Updates: Functional upgrades can be released without affecting other components. Each function or service can have its own release pipeline, list of enhancements, and priority for feature releases.
Error Micro-segmentation: Error in one service will be isolated within that service. Faults will not propagate to other functions as they are modularized and separated. Also, releasing an update to address the issue is quicker and more efficient in this architecture.
Parallel Development & Domain Expertise: Each service can have domain specific experts working on it. In the case of monolithic applications, the entire application stack needs to be updated for feature releases, as well as development is stymied because of complexity and interdependencies.
Reduced Deployment Time: Individual services can be deployed with a focus on function specific features and environments. Development, testing, and pipeline release is for smaller modules rather than the entire application stack. Frequent updates can be made to individual services, and those updates can be deployed in production much faster.
If you need assistance in determining the feasibility of transforming your applications from monolithic to microservices architecture, Keyva can help. Associates at Keyva have worked with many different organizations in various verticals to help in application modernization projects. These include things like refactoring existing applications, adding a wrapper over current applications so they can be consumed easily by DevOps processes, and more. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at info@keyvatech.com. Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => What is Microservices Architecture?
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[post_content] => If you've tried to install Kong from scratch, you know that it is a little more involved than what is posted on the official website guide. There are some pre-requisites that the system needs to be set up with, in order to make sure the service installs and runs successfully. Let us take a look at the steps involved in setting up a Kong instance from the ground up. This guide is valid for RHEL 7, PostgreSQL 11, and Kong 0.14.1. We will set up Kong on a VMware virtual machine. If you are using another hypervisor, the steps for hardware configurations may differ slightly. First, let us take a look at the pre-requisite steps that need to be addressed before we begin our work.- RHEL 7 VM with minimum/base packages
- A valid Subscription with RedHat with appropriate entitlements
- Static IP configuration set up
- /etc/hosts configured for name resolution
- /etc/hostname set up with the fully qualified domain name
- SSH Keys copied on your local workstation (optional)
- Nameserver set up
- firewalld service disabled
- Yum enabled
- Non-root user set up for sudo access (optional)
- VMware tools package installed
Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => Installing Kong: A First Day Guide
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[post_date] => 2019-01-17 20:45:17
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[post_content] => We've all heard that containers and cloud technologies help us avoid lock-ins. While most of that is true, you could be unintentionally locking yourself to a particular technology or framework, if you are not careful. Let's talk about some important aspects to consider, when you are evaluating a cloud or a container platform. 1. Compile a list the features you want a platform to have, based on your requirements Your needs to consume a hybrid cloud architecture may be different from other organizations. The vision of having more flexibility and agility may be the same, but your workloads are different. It is important to know the in-depth architecture of the applications and workloads you want to live in the cloud or container environment. 2. Support You may have thousands of commercial-off-the-shelf applications, and hundreds of custom built ones. Some application vendors may not support containerizing their applications. There could be regulatory or other limitations that prevent certain legacy applications from being supported on containers. Depending upon how critical and integral such applications are to your business, you may want to consider newer offerings that make the full use of cloud and containerization benefits like microservices architecture. 3. Licensing and Exit costs Platform cost is always a factor, no matter the size of the organization. One of the native benefits that containerization provides, is the ability to control costs – assuming your applications are architected accordingly. For example, a cloud-native application can be scaled in to public cloud for just the webtier when necessary (assuming microservices architecture) rather than spinning up large capacity VMs on demand in the cloud for monolithic applications. Another important consideration is around how the platforms are priced. A platform that prices based on worker nodes may be more feasible for an organization that plans on developing hundreds or thousands of microservices, and making them run efficiently on worker nodes. A platform priced based on the number of applications containerized may be more feasible for monolithic applications. You will need to take in to account how your organization plans to scale the containerizing of applications, and what the application architectures will be like. Although you will be making a decision based on long term usage of the platform, it is always a good idea to see what the exit costs will be once you decide to move away from the chosen platform. As an exercise, see if you can figure out what changes would need to be made to the application architecture, the deployment architecture, resource skill sets, and more, if you were to move away from the chosen platform. That can then serve as a yardstick for how locked-in you might be, upon choosing a particular solution. There will always be some level of customization effort an lock-in, no matter which solution you choose – but the idea would be to minimize the monetary and non-monetary exit costs. 4. Detailed architecture knowledge about your applications Nobody wants to make major technology adoption decisions based on hearsay. It is critical to know the detailed application architectures, as that will guide you towards choosing the right solution. If the bulk of your application workloads are legacy monolithic applications, the primary tangible benefit obtained by containerizing them will be horizontal scaling. This is assuming the application vendors will support the containerized versions, the applications will allow multiple instances running within the same sub-net, the application can be used with a load balancer, and so on. To leverage the self-healing and other native capabilities offered by containers, the cloud-native applications developed in a microservices architecture would be an ideal workload. But in the real-world, there is always going to be a mix of legacy and newer workloads. For highly regulated industries like healthcare, insurance, banking, and others – legacy applications may form the biggest chunk of their workloads. If you have the time, it would be ideal to do a cost-benefit analysis for legacy workloads to compare the benefits offered by horizontal scalability and the like, before choosing to move forward with containerization. 5. Implementing vendor agnostic tools, and processes, refactoring if necessary Choosing a platform is just one piece of this complex puzzle involving the rollout of new technologies. There are critical tasks pre- and post- containerization, that also need to be accounted for. For example, how easy or difficult will it be to refactor the existing application for a chosen platform? How easy or difficult will it be to monitor the performance of the chosen platform? How easy or difficult will it be to integrate the chosen platform with other toolsets? Will there be changes needed to your existing processes or toolsets, so they can work effectively with the chosen platform? The end goal for most organizations is to have an easy transition path into Cloud and Container platforms – and to use these platforms as effective tools for their IT teams and business in general. This vision can be best achieved by having a vendor agnostic strategy, and avoiding vendor lock-ins wherever possible. Associates at Keyva have helped multiple organizations assess their application readiness and move them in to containerized and cloud environments. Keyva also helps with the pre- and post- containerization steps. These include things like refactoring existing applications, adding a wrapper over current applications so they can be consumed easily by DevOps processes, and more. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at info@keyvatech.com Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => Avoid Cloud & Container Vendor Lock-In
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[post_content] => You might've heard this before – configuration management database (CMDB) should be the single source of truth. But what does that mean? And how can you achieve it? With all the different third party applications in your environment, you might think it could be a gargantuan effort to consolidate all the data in to the CMDB. It may be a decent amount of work, but the reality is that it is easier than what most would anticipate. By taking the 3 step approach below, you can come close to configuration management database nirvana – a current and accurate CMDB: Step 1: Develop, and agree on a configuration management database schema, and the necessary mappings This step requires the IT Service Management (ITSM) teams, and the various business units to agree on a configuration management database schema and how it will be organized. ITSM teams would create data mappings – to help map data captured from various software components in the environment, into the specified fields within the CMDB schema. This would also include activities around customization of various fields within the CMDB forms, and customizations around API access. Step 2: Integrate and Automate Integrate the configuration management database with all sources of data as per the identified data mappings. This can be done by leveraging existing integrations, or by creating new ones. The population of data within the CMDB can be done as part of an extract-transfer-load process (retrospectively), or as part of the creation of a CI using automation (prospectively). The process of CMDB population is a multi-step process, whereby data is captured via one of the discovery tools and automatically updated within the CMDB. Automation of CI population also helps create relationships between CIs and Change tickets or Incident tickets, thereby making the review process for Change Advisory Boards much easier. Step 3: Optimize and Reconcile Once the data is in the configuration management database, it is important to make sure it is accurate. Given that many different sources of data may compete for the same target field within the CMDB, weights can be assigned to each source to improve accuracy. For example, the asset tag of a device may have a higher weightage when that data is coming from a discovery tool, but the CPU information captured within a configuration management system can be trusted more than any other source. Furthermore, the data captured from all the various sources should be put in staging datasets. It is up to the administrators of the system, to define rule sets and reconciliation rules that will automatically filter the required data in to production data set for consumption. The above may seem like oversimplification of the tasks required to have a fully functional CMDB, but many organizations have successfully adopted a version of this breakdown. It is highly likely that the most time will be spent upfront during the configuration management database schema and data mapping exercise. By investing time and effort towards having an accurate CMDB, organizations can effectively understand the various configurations and their relationships within in the environment, and thereby easily track and manage them. Associates at Keyva have been helping customers set up and optimize their ITSM and CMDB systems for the past two decades. We've also helped organizations develop integrations between configuration management database and third party application software so as to accelerate the population of the CMDB, and to keep it current and accurate. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at info@keyvatech.com Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => 3 Steps to Achieving Configuration Management Database (CMDB) Nirvana
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[post_content] => If you have tried setting up a DevOps pipeline to achieve continuous deployment, and ran into configuration drift issues - you will know the pains quite well. A consistent and uniform configuration is a prerequisite to any automation. If the automated workflow finds that the target configuration is not as anticipated, it will either take the exception route, or revert to notification for manual intervention. What is configuration drift? When a given system configuration or an application configuration changes from the "blessed" or "vetted" state, to another state, it is called configuration drift. For example, if the IT team provides web server instance with a preset configuration file, and as part of the application deployment process or application customization process, that configuration is changed – that constitutes a configuration drift. Similar drifts can occur at the OS level, for packages or commercial software. Why does it matter? It is important to mitigate and remediate any configuration drifts because without it, the environment will become unmanageable, especially as you scale. Consider the web server example above, when there is an application outage in your production environment. As part of finding the root cause of failure, you will now also need to walk back the steps of every configuration changed from the original version, to rule out any issues caused because of those changes. This can cause time and efforts to be deployed towards tangential activities. With consistent deployment automation, you can confidently evaluate the issues plaguing the core application, given all other things to be constant. Another reason that reducing or preventing configuration drift is paramount, is to make sure that any additional deployments on the base tier can be automated. In the above example, it is much easier to automate application deployment on a web server delivered as PaaS, than deploying the same application on a web server that might've been customized, or drifted from its desired state. How to mitigate or prevent configuration drift? There are many ways you can address the resolution of configuration drift. The common factor for all scenarios is to make sure the deployments are automated. Firstly, you'd have to define what constitutes a configuration drift. For example, if you are providing your customers with IaaS machines, would adding a new printer constitute configuration drift? Or would that be classified under allowed customizations, that have no material effect on your service delivery? The rules of configuration will need to be defined. Secondly, you'd want to automate the deployments of your builds and configurations. This could mean using an orchestration framework to deploy the desired service through a self-service catalog, or another automated mechanism. Thirdly, you'd want to make sure that the release and update process for various infrastructure and application components flows through a source control management (SCM) system. Any and all deployments should pick up the latest version of the configs from SCM. And by deploying a systems configuration management solution, you can now check the configuration states of your target systems against the latest versions of those configurations in SCM. There are many other steps you can take to mitigate configuration drift, depending upon the severity of the drift and the penalty you pay for not addressing it. Keyva has helped several organizations address the common challenge of preventing configuration drift. There are several processes and tools an organization can use to address their customized needs for preventing configuration drifts in their infrastructure and applications. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at info@keyvatech.com Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => Configuration Drift : The Bane of Continuous Deployments
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[post_content] => Many organizations have started utilizing DevOps practices and tools for data warehousing and data lake setups. Data Analysts and Database Managers can follow DevOps practices for managing updates and new database releases across various environments in a uniform fashion, to produce repeatable results. Just like application teams create and manage the CI/CD pipeline for applications, the data that these applications consume can have its own release pipeline that is managed by the database teams. In many cases, cloud based data warehousing platforms provide the ability to host the applications that consume this data, all within the same environment. Applications that consume data housed in a data warehouse may also leverage Kafka or other DevOps tools to achieve low latency query performance. As you release updates to your applications, you may also need to account for the updates to the service bus layer and the database layer. Continuous deployment and continuous integration becomes all the more important. Data teams that institute DevOps practices and tools for data warehousing can promote an agile culture within their silos. This includes the process of fetching or discovering the data for data warehousing, the process of making sure it is current and accurate for the consuming applications, and the process of organizing it for data mining and analysis. You can apply DevOps practices and policies to data automation (just like infrastructure automation). Starting from self-service models to request new data instances, to requesting updates, and other data lifecycle steps. There are many organizations that have built entire data platforms on containers. For infrastructure and database teams, it is imperative to provide data "as-a-service" with measured and tracked SLAs and costs – whether these services are provided on container platforms or otherwise. Public cloud platforms have made it easy for consumers to leverage SaaS data warehousing solutions. Using DevOps practices do not have to be limited to providing the underlying infrastructure or service, but can also be applied to the building of reports. Jenkins automation can be used to release database updates, integration tools can be used to fetch the relevant data from multiple sources to populate the target systems, and opensource tools like Grafana can be used for dashboards. Primary objective of such a setup would be to capture data from various components and locations within the environment to a centralized location via ETL, and process that data to produce business intelligence. When bringing data in from multiple sources for data warehousing, the exercise of data mapping and data reconciliation and sanitization usually take the most time and effort upfront. Architectural considerations also include the paradigm of monitoring the data warehouse components, as well as the data within it. Data processing engines like Hadoop MapReduce or Spark, along with the database serving platforms form the core components of any data warehouse setup. By implementing the best practices architecture, and tuning specifically for your environment, you can optimize your data warehouse setup to achieve a balance between performance and cost. Various industry use cases like fraud prevention in banking, storing health records and doctors notes in healthcare, customer profiling for retail, real time streaming in media, and others, have already leveraged the benefits provided by data lakes for capturing and storing unstructured data, and data warehousing for structured data. With the adoption of blockchain technologies, the relevance of Big Data is only anticipated to grow. Most enterprises depend heavily on applications for their business, and thereby have adopted agile processes for application releases. Combining the consumption of Big Data with emphasis on extracting relevant and accurate data at the right time, is paramount for business critical applications. The adoption of DevOps practices and tools for data warehousing within data teams is still in its nascent stage, but is being picked up by more and more data experts every day. If you need assistance with data warehousing to move your disparate data from various sources, or need help assessing the feasibility of a data warehouse platform without substantially affecting your business critical applications, Keyva can help. Associates at Keyva have worked with many different organizations in various verticals to help in data migration and application modernization projects. These include things like creating a data migration factory, creating ETL strategies with data mapping, refactoring existing applications, adding a wrapper over current applications so they can be consumed easily by DevOps processes, modifying existing applications to consume data from SaaS platforms, and more. If you'd like to have us review your environment and provide suggestions on what might work for you, please contact us at info@keyvatech.com.Anuj joined Keyva from Tech Data where he was the Director of Automation Solutions. In this role, he specializes in developing and delivering vendor-agnostic solutions that avoid the “rip-and-replace” of existing IT investments. Tuli has worked on Cloud Automation, DevOps, Cloud Readiness Assessments and Migrations projects for healthcare, banking, ISP, telecommunications, government and other sectors. During his previous years at Avnet, Seamless Technologies, and other organizations, he held multiple roles in the Cloud and Automation areas. Most recently, he led the development and management of Cloud Automation IP (intellectual property) and related professional services. He holds certifications for AWS, VMware, HPE, BMC and ITIL, and offers a hands-on perspective on these technologies. Like what you read? Follow Anuj on LinkedIn at https://www.linkedin.com/in/anujtuli/ [post_title] => DevOps and Data Warehousing
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February 28, 2019
DevOps and Data Warehousing
Many organizations have started utilizing DevOps practices and tools for data warehousing and data lake setups. Data Analysts and Database Managers can follow DevOps practices for managing updates and new ...
- By : Anuj Tuli
- Blog & Insights
- 0 Comments
February 25, 2019
How to set up Hadoop two node cluster and run MapReduce jobs
This write-up walks through setting up a two node Hadoop v3.1.1 cluster, and running a couple of sample MapReduce jobs. Prerequisites: Two machines set up with RHEL 7. You could ...
- By : Anuj Tuli
- Blog & Insights
- 0 Comments
February 19, 2019
Installation Guide: Ansible & Ansible Tower – Day 1
This technical guide will walk you through the installations of Ansible v2.4.2.0, an open source configuration management and deployment tool, and Ansible Tower (web layer for Ansible) v3.4.1 on a ...
- By : Anuj Tuli
- Blog & Insights
- 0 Comments
January 30, 2019
What is Microservices Architecture?
You may have heard the term Microservices when talking about applications, and some cases even when talking about underlying infrastructure. If you’ve worked with any OOPS language, you must be ...
- By : Anuj Tuli
- Blog & Insights
- 0 Comments
January 21, 2019
Installing Kong: A First Day Guide
If you’ve tried to install Kong from scratch, you know that it is a little more involved than what is posted on the official website guide. There are some pre-requisites ...
- By : Anuj Tuli
- Blog & Insights
- 0 Comments
January 17, 2019
Avoid Cloud & Container Vendor Lock-In
We’ve all heard that containers and cloud technologies help us avoid lock-ins. While most of that is true, you could be unintentionally locking yourself to a particular technology or framework, ...
- By : Anuj Tuli
- Blog & Insights
- 0 Comments
January 15, 2019
3 Steps to Achieving Configuration Management Database (CMDB) Nirvana
You might’ve heard this before – configuration management database (CMDB) should be the single source of truth. But what does that mean? And how can you achieve it? With all ...
- By : Anuj Tuli
- Blog & Insights
- 0 Comments
January 11, 2019
Configuration Drift : The Bane of Continuous Deployments
If you have tried setting up a DevOps pipeline to achieve continuous deployment, and ran into configuration drift issues – you will know the pains quite well. A consistent and ...
- By : Anuj Tuli
- Blog & Insights
- 0 Comments