In our introductory post we discussed the typical structure and usual components of a modern data platform.

A very common component of any Data Lake and Data Warehouse implementation is what we often call the “Cold Storage” tier. This is where, or rather how, the vast majority of data is persisted in a cheaper storage solution, ideally in a way that still allows it to be queried. Putting data in “cold storage” assists in reducing the total costs of the data platform and trading off query speed with significantly lower costs.

In this post we are going to focus on that layer of cold storage, understand how it usually works, and try to map the gaps that exist today and offer our wishlist for more modern implementations which will serve the future versions of data platforms.

The best place to start is with the convention most large data platforms in use today are using, often referred to as “Hive Tables”, or the Hive Table Format.

The Hive Table Format

Apache Hive is a SQL engine to query Big Data. It was originally built to translate SQL statements into Hadoop MapReduce jobs, and contin...

We live in an era of data. Data is in every organization’s strategy, every engineer’s job description, and every CIO’s dreams (or nightmares). Day in, day out, more data collectors and more data generators are being built. The collectors are observability tools and data platforms, and the generators...

Cloud Computing today is accessible by everyone: anyone can launch a EC2 instance on AWS or write entire systems using Serverless technologies without launching even a single VM. The on-going competition between cloud giants AWS, Google Cloud Platform and Microsoft Azure keeps bringing prices down a...

Presto is a state of the art Distributed SQL Query Engine for BigData, enabling efficient querying on cold data and various data sources. With extended SQL language and features like geospatial queries, joins between different data sources (SQL to join data from HDFS, Elasticsearch, and Kafka anyone...

A few weeks ago I gave a talk on Google Campus TLV on deploying and running Elasticsearch on Kubernetes - best practices and various gotchas. The video for the talk is below.

There is also a blog post with most of the technical details here.

On that note, you should definitely check out Elastic's re...

Kubernetes is quickly becoming the de-facto standard for running systems in the cloud and on-premises, and in the last couple of years we at BigData Boutique have had to deploy and support quite a few Elasticsearch clusters on Kubernetes.

Now is probably a good time to reflect on this and have a hig...

I just read the recent announcements from AWS on their fresh "Open Distro" of Elasticsearch. Statements like "Keeping Open Source Open" and "it has created uncertainty about the longevity of the open source project as it is getting less innovation focus" drew my attenti...

I just published Easily deploying Presto on AWS with Terraform, but ignored a very important question: AWS offers Athena for SQL over S3, which is essentially a Presto deployment managed by AWS. Why not just use AWS Athena instead of going through the trouble of deploying your own cluster?

I probabl...

In the past year or so we have been involved in creating several data-lakes and data-warehouses - some from scratch and some in a brownfield environment where we had to either switch from traditional SQL clusters or run side-by-side with them.

Our tool of choice for running SQL on top of "BigDa...