DevOps is a cultural shift with immediate focus on maximising the business value by opting better communication, collaboration and feedback within and across IT development and operation teams. Infrastructure as Code (IaC) is a key element of DevOps philosophy with benefits for both development and operation teams. The term infrastructure as code is sometimes also referred to as programmable infrastructure.

Ever since the Amazon introduced the AWS Lambda service at AWS re:Invent 2014, a variety of new applications for the service has emerged which highlights tremendous potential and traction for the AWS Lambda service. Over last one year, Amazon has been actively working towards integrating other AWS services with AWS Lambda.

Amazon today announced Amazon Elasticsearch Service (Amazon ES) - a fully managed Elasticsearch service which can support your real-time distributed search requirements. I am not sure about others, but for me a this is a big deal and can be a game changer. I have worked with both Splunk and Elasticsearch. On the one hand, Splunk is definitely a superior product and well-packaged solution for operational intelligence.

I have been playing with Apache Drill for quite some time now. In layman’s terms, Apache Drill is SQL query engine which can perform queries against any type of data store - in particular any non-relational data store. Apache Drill’s ability to query against raw data files stored on the cloud storage platforms such as Amazon S3, Azure Blob Storage, Google Cloud Storage makes it a really attractive and cost effective query engine.

We are observing a new era in database technology space, drop-in replacements with order of magnitude improvement in performance over their counterparts. First Amazon Aurora and now Scylla DB. Although the concept of drop-in replacements is not something new but most recently these substitutes started pushing the performance boundaries using completely overhauled architecture.

If you work with AWS CloudFormation on a day-to-day basis, I think you might be interested in visualising your AWS CloudFormation templates. CloudFormation stack visualization can be quite handy for lifecycle management of your AWS deployment. In addition, visualization can be immensely helpful in change management and governance.

Aurora is a new offering from Amazon Relational Database Service (RDS). It is advertised as drop-in replacement for the MySQL - full compatibility with MySQL 5.6. Aurora is designed for fault-tolerance, availability and storage elasticity. It is a relational database and a highly cost effective one.

Currently the majority of cloud based database and data warehouse services are provisioned with fixed storage and compute resources. Resizing of resources cannot be performed without compromising availability and performance. This means service users typically end up with over-provisioned under-utilised expensive resources to accommodate possible peak demand. In the worst case, under-provisioned resources unable to handle sudden work overloads.

In 2005 Stonebraker et al. published a paper that outlined 8 key requirements for stream processing architecture. These key requirements can be easily translated into building blocks of stream processing architecture. Although, this article dates before systems such as Apache Kafka, Amazon Kinesis, Apache Spark, Apache Storm, etc. most of the requirements are still relevant.

For those who are not familiar with MVC (Model–view–controller), MVC is a software architectural pattern primarily used for web development and to implement user interfaces. Ruby on Rails (RoR), Django, Spring MVC, ASP .net, Symfony, Zend, Express are some of the most popular MVC frameworks currently available for rapid web development. Over the time web development community has immensely benefited from these MVC frameworks.

In a recent InfoWorld article, FoundationDB’s Stephen Pimentel explained the rise of the multimodel database. A multimodel database uses a single data store to support multiple data models. For instance, FoundationDB tries to map documents, graphs, and relational tables to a key-value data store.