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How to "Democratize" the Responsibility for Data Quality Across your Organization

 

 

Writing endless data transformations wasn't sustainable for an engineering team handling hundreds of inputs. Here's how Clover Health enabled their business users to help.

It's rare to find an ETL system that's completely static. As organizations change and grow they develop new business requirements. Because of this their data pipelines must change and adapt, ultimately becoming more robust and full-featured. Yet constant development can make already brittle ETL systems seem even more fragile.

Furthermore, systems with large numbers of different types of inputs bring special challenges - building, testing and managing an exploding number of data transformations can become a daunting project for the engineering team. 

The Clover Health ETL system supports hundreds of inputs and more than 500 custom transformations in production as well as a large number of custom connections between their different ETL pipelines. When hearing about the magnitude of the system, one might rightfully wonder, "how does Clover guarantee and maintain data quality across so many different inputs and transforms?"

Exploring the development trajectory of Clover's system makes for a fascinating story; hearing about their data team's successes and pitfalls are illustrative lessons to other engineers as they seek to increase the robustness of their own ETL systems.

The Future of Distributed Databases is Relational

 

 

What if developers could ditch their No-SQL solutions and still get scalability from a more traditional relational datastore?

I've been noticing an interesting pattern recently where developers seem to be rejecting some of the newer, more en vogue data stores with limited functionality and use-cases (while promising easier scale) and returning to the comfortable tried-and-true paradigm of relational databases. It seems that we've hit a watershed point where developers finally believe they don't necessarily need to make a trade-off between database features on one hand and easy scalability on the other.

One such company enabling this return to the golden era of of RDBMS is Citus Data. Citus is blazing a trail in 'cloud-proofing' the gold standard of relational databases, PostgreSQL, through extensions that allow their customers to achieve much easier horizontal scalability than ever before. 

How Dremio Uses Apache Arrow to Increase the Performance

 

(Image source: http://arrow.apache.org/)

What if all the best open-source data platforms could easily share, ("ahem,") data with each other?

As data has proliferated and open-source software (OSS) has continued to dominate both the stacks and the business models of the top tech companies in the world, the number of different types of data platforms and tools we've seen emerge has accelerated.

Having a hard time keeping up with the differences between Kudu, Parquet, Cassandra, HBase, Spark, Drill and Impala? You're not alone, and obviously this is one of the reasons we bring together top OSS contributors to these platforms to share at DataEngConf.

But there's one new innovation that attempts to bind all the above projects together by enabling them to share a common memory format. It's a new top level Apache Project called Arrow that aims to dramatically decrease the amount of wasted computation that occurs when serializing and deserializing memory objects. The serialization pattern is commonly used when building analytics applications that interact between data systems which have their own internal memory representations.  

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