Etleap Launches Snowflake Integration

I am pleased to announce our integration with Snowflake. This is the second data warehouse we support, augmenting our existing Amazon Redshift data warehouse and our S3/Glue data lake offering. 

Etleap lets you integrate all your company’s data into Snowflake, and transform and model it as necessary. The result is clean and well-structured data in Snowflake that is ready for high-performance analytics. Unlike traditional ETL tools, Etleap does not require engineering effort to create, maintain, and scale. Etleap provides sophisticated data error handling and comprehensive monitoring capabilities. Because it is delivered as a service, there is no infrastructure to maintain.

 

2019.05.07 - Etleap Product Graphic

 

Like any other pipeline set up in Etleap, pipelines to Snowflake can extract from any of Etleap’s supported sources, including databases, web services, file stores, and event streams. Using Etleap’s interactive data wrangler, users have full control over how data is cleaned, structured, and de-identified before it is loaded into Snowflake. From there, Etleap’s native integration with Snowflake is designed to maximize flexibility for users in specifying attributes such as Snowflake schemas, roles, and cluster keys. Once the data is loaded, Etleap’s SQL-based modeling features can be used to further improve the usability and performance of the data for analytics.

Not only does Etleap’s integration with Snowflake provide a seamless user experience, it is also a natural fit technically. Etleap is built on AWS and stores extracted and transformed data in S3. Since Snowflake stores data in S3, loading data into Snowflake is fast and efficient. Architecturally, part of what differentiates Snowflake is its separate, elastic scaling of compute and storage resources. Etleap is built on the same principle, thus enabling it to overcome traditional bottlenecks in ETL by scaling storage and compute resources for extraction and transformation separately and elastically. By taking advantage of AWS building blocks we are able to provide a powerful yet uncomplicated data analytics stack for our customers. 

Etleap is devoted to helping teams build data warehouses and data lakes on AWS, and we offer both hosted and in-VPC deployment options. Like Snowflake, Etleap takes advantage of AWS services such as S3 and EC2 to provide performance and cost benefits not possible with traditional ETL solutions.

As more and more teams building analytics infrastructure on AWS want to use Snowflake as their data warehouse, offering support for Snowflake was a natural next step for us. 

If you would like to explore building a Snowflake data warehouse with Etleap, you can sign up for a demo here.

 

New Features: Models and History Tables

I’m excited to tell you about two new features we’re launching today: Models and History Tables.

 

Models

Etleap has long supported single-source transformations through data wrangling. This is great for cleaning, structuring, and filtering data, and for removing unwanted data, such as PII, before it is loaded to the destination. Today, we’re announcing the general availability of models, which enable transformations expressed as SQL queries. Two primary use cases for models are combining data from different sources to build data views optimized for analytics, and aggregating data to speed up analytics queries.

Etleap models are Redshift tables backed by SQL SELECT queries that you define, running against data that has been loaded to Redshift. Etleap creates tables that are the result of these SELECT queries, and updates these tables incrementally or through full refreshes. Etleap triggers updates based on changes to dependent tables, or on a schedule.

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History Tables

For regular pipelines into Redshift, Etleap fetches new and updated records from the source. Following transformation, new rows are appended to the destination table, and updated rows are overwritten. This update strategy is known as type-1 Slowly Changing Dimensions in data warehouse speak.

Sometimes it’s useful to be able to go back in time and query the past state of a record, or to be able to investigate how a record has changed over time. For this, Etleap now provides the ability to retain the history of a record collection. For this, the technique known as type-2 Slowly Changing Dimensions is often used. Here’s how it works in Etleap: An end-date column is added to the table. When a record is initially inserted into the destination table, this column’s value is null. Whenever the record is changed in the source, instead of overwriting the existing record in the destination table, a new row is appended instead with a null end-date value. The existing record’s end-date value is set to the new record’s update timestamp.

Starting today, history tables are available for all pipelines from sources that have a primary key and an update timestamp. To get a history table, check the ‘retain history’ box during single or batch pipeline setup.

 

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Want to see these features in action? Request a demo here!

Scaling Etleap with funding from First Round Capital, SV Angel, and more

Today we’re excited to share that we’ve raised $1.5M from First Round Capital, SV Angel, Liquid2, BoxGroup, and others to continue to scale our enterprise-grade ETL solution for building and managing cloud data warehouses.

ETL has traditionally been associated with expensive projects that take months of custom development by specialized engineers. We started Etleap because we believe in a world where analytics teams manage their own data pipelines, and IT teams aren’t burdened with complex ETL infrastructure and tedious operations.

Etleap runs in the cloud and requires no engineering work to set up, maintain, and scale. It helps companies drastically lower the cost and complexity of their ETL solution and improve the usefulness of their data.

Over the past few years we’ve spent a lot of time with analytics teams in order to understand their challenges and have built features for integration, wrangling, and modeling. It’s a thrill to see data-driven customers, including Airtable, Okta, and AXS, use them. Their analytics teams are pushing the boundaries of what’s possible today, and we’re hard at work building features to help bring their productivity to new levels.

 


 

Curious how Etleap can solve your analytics infrastructure challenges? Click here to get a demo of Etleap!