SPEEDING UP ETLEAP MODELS AT AXS WITH AMAZON REDSHIFT MATERIALIZED VIEWS

This blog post was written in partnership with the Amazon Redshift team, and also posted on the AWS Big Data Blog.

The materialized views feature in Amazon Redshift is now generally available and has been benefiting customers and partners in preview since December 2019. One customer, AXS, is a leading ticketing, data, and marketing solutions provider for live entertainment venues in the US, UK, Europe, and Japan. Etleap, an Amazon Redshift partner, is an extract, transform, load, and transform (ETLT) service built for AWS. AXS uses Etleap to ingest data into Amazon Redshift from a variety of sources, including file servers, Amazon S3, relational databases, and applications. These ingestion pipelines parse, structure, and load data into Amazon Redshift tables with appropriate column types and sort and distribution keys.

Improving dashboard performance with Etleap models

To analyze data, AXS typically runs queries against large tables that originate from multiple sources. One of the ways that AXS uses Amazon Redshift is to power interactive dashboards. To achieve fast dashboard load times, AXS pre-computes partial answers to the queries dashboards use. These partial answers are orders of magnitude smaller in terms of the number of rows than the tables on which they are based. Dashboards can load much faster than they would if they were querying the base tables directly by querying Amazon Redshift tables that hold the pre-computed partial answers.

Etleap supports creating and managing such pre-computations through a feature called models. A model consists of a SELECT query and triggers for when it should be updated. An example of a trigger is a change to a base table, that is, a table the SELECT statement uses that defines the model. This way, the model can remain consistent with its base tables.

The following screenshot shows an Etleap model with two base table dependencies.

Etleap represents their models as tables in Amazon Redshift. To create the model table, Etleap wraps the SELECT statement in a CREATE TABLE AS (CTAS) query. When an update is triggered, for example, due to base table inserts, updates, or deletes, Etleap recomputes the model table through the following code:

CREATE TABLE model_temporary AS SELECT …
DROP TABLE model;
RENAME TABLE model_temporary TO model;

Analyzing CTAS performance as data grows

AXS manages a large number of Etleap models. For one particular model, the CTAS query takes over 6 minutes, on average. This query performs an aggregation on a join of three different tables, including an event table that is constantly ingesting new data and contains over a billion rows. The following graph shows that the CTAS query time increases as the event table increases in number of rows.

There are two key problems with the query taking longer:

  • There’s a longer delay before the updated model is available to analysts
  • The model update consumes more Amazon Redshift cluster resources

To address this, AXS would have to resort to workarounds that are either inconvenient or costly, such as archiving older data from the event table or expanding the Amazon Redshift cluster to increase available resources.

Comparing CTAS to materialized views

Etleap decided to run an experiment to verify that Amazon Redshift’s materialized views feature is an improvement over the CTAS approach for this AXS model. First, they built the materialized view by wrapping the SELECT statement in a CREATE MATERIALIZED VIEW AS query. For updates, instead of recreating the materialized view every time that data in a base table changes, a REFRESH MATERIALIZED VIEW query is sufficient. The expectation was that using materialized views would be significantly faster than the CTAS-based procedure. The following graph compares query times of CTAS to materialized view refresh.

Running REFRESH MATERIALIZED VIEW was 7.9 times faster than the CTAS approach—it took 49 seconds instead of 371 seconds on average at the current scale. Additionally, the update time was roughly proportional to the number of rows that were added to the base table since the last update, rather than the total size of the base table. In this use case, this number is 3.8 million, which corresponds to the approximate number of events ingested per day.

This is great news. The solution solves the previous problems because the delay the model update caused stays constant as new data comes in, and so do the resources that Amazon Redshift consume (assuming the growth of the base table is constant). In other words, using materialized views eliminates the need for workarounds, such as archiving or cluster expansion, as the dataset grows. It also simplifies the refresh procedure for model updates by reducing the number of SQL statements from three (CREATE, DROP, and RENAME) to one (REFRESH).

Achieving fast refresh performance with materialized views

Amazon Redshift can refresh a materialized view efficiently and incrementally. It keeps track of the last transaction in the base tables up to which the materialized view was previously refreshed. During subsequent refreshes, Amazon Redshift processes only the newly inserted, updated, or deleted tuples in the base tables, referred to as a delta, to bring the materialized view up-to-date with its base tables. In other words, Amazon Redshift can incrementally maintain the materialized view by reading only base table deltas, which leads to faster refresh times.

For AXS, Amazon Redshift analyzed their materialized view definitions, which join multiple tables, filters, and aggregates, to figure out how to incrementally maintain their specific materialized view. Each time AXS refreshes the materialized view, Amazon Redshift quickly determines if a refresh is needed, and if so, incrementally maintains the materialized view. As records are ingested into the base table, the materialized view refresh times shown are much faster and grow very slowly because each refresh reads a delta that is small and roughly the same size as the other deltas. In comparison, the refresh times using CTAS are much slower because each refresh reads all the base tables. Moreover, the refresh times using CTAS grow much faster because the amount of data that each refresh reads grows with the ingest rate.

You are in full control of when to refresh your materialized views. For example, AXS refreshes their materialized views based on triggers defined in Etleap. As a result, transactions that are run on base tables do not incur additional cost to maintain dependent materialized views. Decoupling the base tables’ updates from the materialized view’s refresh gives AXS an easy way to insulate their dashboard users and offers them a well-defined snapshot to query, while ingesting new data into base tables. When AXS vets the next batch of base table data via their ETL pipelines, they can refresh their materialized views to offer the next snapshot of dashboard results.

In addition to efficiently maintaining their materialized views, AXS also benefits from the simplicity of Amazon Redshift storing each materialized view as a plain table. Queries on the materialized view perform with the same world-class speed that Amazon Redshift runs any query. You can organize a materialized view like other tables, which means that you can exploit distribution key and sort columns to further improve query performance. Finally, when you need to process many queries at peak times, Amazon Redshift’s concurrency scaling kicks in automatically to elastically scale query processing capacity.

Conclusion

Now that the materialized views feature is generally available, Etleap gives you the option of using materialized views rather than tables when creating models. You can use models more actively as part of your ETLT strategies, and also choose more frequent update schedules for your models, due to the performance benefits of incremental refreshes

For more information about Amazon Redshift materialized views, see Materialize your Amazon Redshift Views to Speed Up Query Execution and Creating Materialized Views in Amazon Redshift.

by Christian Romming, Prasad Varakur (AWS), and Vuk Ercegovac (AWS)

AWS re:Invent 2019 Roundup

Materialized Views, Amazon Redshift Ready, and more!

Last week Etleap put on another exciting show at AWS re:Invent, where we announced some new features and integrations with AWS services, were interviewed by the tech experts over at “theCUBE,” hosted a session all about data lakes, and most importantly, spoke with countless attendees about ETL. Here’s a roundup of all the Etleap action you may have missed at AWS re:Invent 2019.


Etleap’s booth was a veritable oasis of ETL discussion and Etleap product demos
Amazon Redshift Launches materialized views with help from etleap

Among AWS’ numerous announcements at re:Invent this year was the availability of Materialized Views in preview on Amazon Redshift. The Materialized Views feature is designed to help customers achieve up to 100x faster query performance on analytical workloads such as dashboarding queries from Business Intelligence (BI) tools and ELT data processing. Etleap helped launch this feature by integrating it into a beta version of Etleap Models (let us know if you want to be included in the beta!) and showing that it can give an ~8x performance boost. The Redshift team showcased our results in their chalk talk on “Accelerating performance with Materialized Views.”


Yannis (seated, left) and Vuk (standing, right) from the Amazon Redshift team showcase Etleap at their Redshift Materialized Views Chalk Talk

“We are delighted to have Etleap help launch the Materialized Views feature in Amazon Redshift,” said Andi Gutmans, Vice President, Analytics, Amazon Web Services, Inc. “Amazon Redshift Materialized Views allow customers to realize a significant boost in query performance in ETL pipelines and BI dashboards. By integrating Etleap with this new functionality, customers can seamlessly get the benefits of Amazon Redshift Materialized Views without needing to make any application changes.”

You can read the full Etleap press release about Amazon Redshift Materialized Views here.

Etleap Founder makes the case for more analyst-friendly data lakes, alongside Redshift team

Many Etleap customers use our solution to build their S3/Glue data lakes, so data lakes are a topic we’ve learned a thing or two about over the years. For re:Invent this year, we thought we’d share our data lake expertise with the world by hosting a session alongside the Redshift team entitled “Five data lake considerations with Amazon Redshift, Amazon S3 & AWS Glue.”


Etleap founder and CEO, Christian Romming, led the session focused on data lakes

Have an interest in data lakes yourself? You can check out the session here.

Etleap featured on enterprise tech talk show

After our data lakes session, Founder and CEO of Etleap, Christian Romming, sat down with the hosts of “theCUBE,” re:Invent’s resident technologies interview show. Check it out:

Etleap founder sits down with David Vellante and John Walls of theCUBE
Etleap achieves Amazon Redshift Ready Designation

Distinguishing ourselves in the Amazon Redshift partner ecosystem, we announced that Etleap has achieved the designation of “Amazon Redshift Ready,” a recently announced status among partners who have proven integration with Amazon Redshift.

Etleap was featured in the keynote announcement among a select few debuting partners

“Etleap is proud to achieve Amazon Redshift Ready status,” said Christian Romming, Founder and CEO of Etleap. “Our team is dedicated to helping companies achieve maintenance-free, enterprise-grade ETL by leveraging the agility, breadth of services, and pace of innovation that AWS provides. Our status as an Amazon Redshift Ready partner shows our continued commitment to Amazon Redshift and the AWS ecosystem.”

You can read the full Etleap press release covering the Amazon Redshift Ready announcement here.


This concludes our roundup of the biggest Etleap new stories from AWS re:Invent 2019. Stay tuned for more Etleap trade show news, and for all things ETL you’re already in the right place.

Etleap Achieves Amazon Redshift Ready designation

Recently-announced designation distinguishes Etleap on the Redshift platform

SAN FRANCISCO, Calif. – December 4, 2019 — Etleap announced today that it has achieved the Amazon Redshift Ready designation. This designation recognizes that Etleap has demonstrated successful integration with Amazon Redshift. 

Achieving the Amazon Redshift Ready designation differentiates Etleap as an AWS Partner Network (APN) member with a product integrating with Amazon Redshift and is generally available and fully supported for AWS customers. AWS Service Ready Partners have demonstrated success building products integrated with AWS services, helping AWS customers evaluate and use their technology productively, at scale and varying levels of complexity. 

“Etleap is proud to achieve Amazon Redshift Ready status,” said Christian Romming, Founder and CEO of Etleap. “Our team is dedicated to helping companies achieve maintenance-free, enterprise-grade ETL by leveraging the agility, breadth of services, and pace of innovation that AWS provides. Our status as an Amazon Redshift Ready partner shows our continued commitment to Amazon Redshift and the AWS ecosystem.”

To support the seamless integration and deployment of these solutions, AWS established the AWS Service Ready Program to help customers identify products integrated with AWS services and spend less time evaluating new tools, and more time scaling their use of products that are integrated with AWS Services.

Etleap is analyst-friendly ETL-as-a-service for Amazon Redshift and Snowflake data warehouses and Amazon S3/AWS Glue data lakes. Etleap replaces time-consuming ETL setup and maintenance with intuitive software and a managed service that automates data pipelines and reduces time to value.

For more information, email info@etleap.com; Follow us on Twitter @etleap; or Like us on Facebook @etleap.


About Etleap: Etleap was founded by Christian Romming in 2013. Before founding Etleap, Romming was the CTO of an ad-tech company, where he recognized the available solutions for building data pipelines required monumental engineering resources to implement, maintain, and scale. Etleap is backed by world-class investment firms First Round Capital, SV Angel, BoxGroup, and Y Combinator. Our mission is to make data analytics teams more productive. Our ETL solution lets analysts build data warehouses without internal IT resources or knowledge of complex scripting languages. This reduces the time of typical ETL projects from weeks to hours, and takes out the pain of maintaining data pipelines over time.

Etleap announces support for Amazon Redshift Materialized Views

Etleap customers will benefit from new technology in Etleap for faster query performance

SAN FRANCISCO, Calif. – December 2, 2019 — Today, Etleap, an Advanced Technology Partner in the Amazon Web Services (AWS) Partner Network (APN) and provider of fully-managed Extract, Load, Transform (ETL)-as-a-service, announced support for Amazon Redshift Materialized Views. The new feature is designed to help customers achieve up to 100x faster query performance on analytical workloads such as dashboarding queries from Business Intelligence (BI) tools and ELT data processing. Because Etleap was built from the ground up to handle data integration for Amazon Redshift users, including orchestration of transformations within Amazon Redshift, the company is uniquely positioned to test this new capability and provide support for it in their product.

“We are delighted to have Etleap help launch the Materialized Views feature in Amazon Redshift,” said Andi Gutmans, Vice President, Analytics, Amazon Web Services, Inc. “Amazon Redshift Materialized Views allow customers to realize a significant boost in query performance in ETL pipelines and BI dashboards. By integrating Etleap with this new functionality, customers can seamlessly get the benefits of Amazon Redshift Materialized Views without needing to make any application changes.”

“For as long as Amazon Redshift has been around, Etleap has been making some of the most complex data pipelines easier and faster for AWS users, so working with the Amazon Redshift team to improve post-load transformations with Amazon Redshift Materialized Views was a perfect fit for us,” said Christian Romming, Founder and CEO of Etleap. “Etleap was designed for AWS and delivers analyst-friendly, enterprise-grade ETL-as-a-service. By collaborating with the Amazon Redshift team on this project, we continue to show our commitment to our customers and AWS, and have taken another major step in our quest to make data integration less of a headache without sacrificing control or visibility — and we couldn’t be more excited.”

Customers value Etleap’s modeling feature, because it allows them to gain advanced intelligence from their data. One challenge for customers is the time it takes to refresh a model when data changes. Amazon Redshift Materialized Views allows Etleap to refresh model tables faster and use fewer Amazon Redshift cluster resources in the process, which frees up more resources for other Amazon Redshift workloads. This allows a customer’s engineering and analyst teams to deliver on the desired outcome more efficiently.

For more information, email info@etleap.com; Follow us on Twitter @etleap; or Like us on Facebook @etleap.


About Etleap: Etleap was founded by Christian Romming in 2013. Before founding Etleap, Romming was the CTO of an ad-tech company, where he recognized the available solutions for building data pipelines required monumental engineering resources to implement, maintain, and scale. Etleap is backed by world-class investment firms First Round Capital, SV Angel, BoxGroup, and Y Combinator. Our mission is to make data analytics teams more productive. Our ETL solution lets analysts build data warehouses without internal IT resources or knowledge of complex scripting languages. This reduces the time of typical ETL projects from weeks to hours, and takes out the pain of maintaining data pipelines over time.

AXS knocks it out of the park with modern data analytics

Learn how Etleap and Looker helped AXS reduce manual ETL work and reporting, allowing them to focus on growth for themselves and their clients.

AXS is a ticketing company for live entertainment

AXS powers the ticket buying experience for over 350 world-wide partners

AXS is a leading ticketing, data, and marketing solutions provider in the US, UK and Europe. The company and its solutions empower more than 200 clients— teams, arenas, theaters, clubs and colleges— to turn data into action, maximize the value of all their events and create joy for fans. It is an enterprise event technology platform that services venues, promoters and sports teams; providing fans the opportunity to purchase tickets directly from their favorite venues via a user-friendly ticketing interface. While customers know them as a destination for tickets, clients recognize AXS for their data services, including transforming, reporting, analyzing, and more.

The data services offered by AXS have always been incredibly helpful for clients. But to make them so valuable, a significant amount of ETL work and reporting was required, which resulted in some challenges for the team. To learn more about those challenges, and how they eventually found a solution in both Etleap and Looker, we spoke with Ben Fischer, the Sr. Director of Business Intelligence and Strategy.

THE CHALLENGES

Ben oversees the Business Intelligence and Strategy team, which manages everything from integrations and building data models to powering the data warehouse and products across AXS. The team’s main objectives are to power AXS’s internal data services, while also delivering data services for clients.

Before Etleap and Looker, the Data Engineering team was spending more and more of their time working on internal and external requests for one-off ingestions and custom data sources. Each data source would take anywhere from half a day to weeks (or even months) to implement, which meant the team was spending most of their time on ETL work, and not enough time on making the data useful.

“With Etleap, we’re able to do the ETL end-to-end and get it directly into the hands of whoever’s trying to use it right away.”

– Ben Fischer, Sr. Director of Business Intelligence

Ben told us, “The whole team was just getting sucked into ETL work constantly, which was not the best use of their time. We wanted to be working on modeling and on the products.”

SEARCHING FOR A BETTER DATA SOLUTION

In order to find the right solutions to fit their needs, the AXS team compared several modern ETL solutions.

When comparing the options, they found that most of the tools were good solutions for getting ETL out of the engineers’ hands, allowing less technical people to consistently bring in the data, while also offering support and monitoring. However, Etleap stood out in two main areas: transformation and transparency.

For AXS, transformation was important because they wanted the ability to not only bring in a new data source, but automatically transform it into something useful for analysts. “Stitch and Fivetran are really focused on the “extract” and “load,” so they’ll bring data in from an outside source and put it into your data warehouse, but they don’t offer much in the way of transformation. You still have to transform it afterwards into something that’s usable, which means you’re still relying on engineering to access the data. With Etleap, we’re able to do the ETL end-to-end and get it directly into the hands of whoever’s trying to use it right away.” said Ben.

Etleap’s data wrangler makes parsing and structuring data take minutes instead of months.

Beyond the transformation aspect, the AXS team was also impressed by Etleap’s level of transparency around reliability. “A lot of the competitors emphasize this idea of 100% reliability. They would say that they would never miss any data, everything would come through perfectly, and you would never have any issues. But we knew that wasn’t the case. No tool is 100% perfect, and when talking with Etleap, they were much more open about what we could expect. They acknowledged that 100% reliability is the objective, but that it’s challenging to achieve in practice, so it’s something they’re continuously working to achieve. Some of the competitors wouldn’t even acknowledge that reliability could possibly be an issue, which makes you feel like they may not support you if anything goes wrong.” said Ben.

To top things off, Etleap was also very straightforward to use. It required very little training and offered reliable support, which meant AXS could get up and running immediately. When the team first started evaluating ETL solutions, they encountered complexity with managing the tools and building integrations. “But with Etleap, it’s pretty straightforward. There’s always somebody available if you need to reach out. That meant we could start using Etleap in just a matter of days, rather than undergoing weeks of training,” said Ben.

Once the team found an ETL solution, it was time to help out the analysts. They looked at a variety of products for business intelligence, and even tried a few different solutions, but Looker stood out in part because it could get report building out of the hands of the analysts. Ben told us, “With Etleap, it was about getting ETL out of engineering. With Looker, it was about getting report building out of analytics, so analysts can spend their time actually forming opinions, defining strategy, doing analyses, and digging into the data, rather than just building reports day in and day out.”

Consistency and confidence are critical to democratizing data, and Looker’s data modeling layer allows people across AXS to pull their own insights and reports very quickly without having to worry about whether the numbers match. This means the Business Intelligence & Strategy team can now stay focused on building models and driving insights, instead of just building reports.

WITH ETLEAP AND LOOKER, THE ENTIRE AXS TEAM IS ABLE TO FOCUS ON HIGH-VALUE TASKS THAT DRIVE THEIR BUSINESS FORWARD.

Since implementing both Etleap and Looker, the impact has positively affected the entire AXS team, as well as their clients.

First, the Data Engineering and Business Intelligence & Strategy teams are spending far less time on manual ETL and reporting work, and much more time on high-value tasks that contribute to internal growth, as well as client successes.

“These tools make our various teams more impactful across the business. For example, if our engineers were just doing all the ETL work manually, we would not be able to do even half of the work that we’re doing to drive the business forward. And the same applies with Looker. Right now, we’ve got people all over the organization looking at reports every day in Looker and answering their own questions about what’s going on with the business.

“Our lives have become much less about pulling reports or bringing in data, and more about really driving value for the company beyond the mundane day to day.” Beyond making life easier and their work more impactful, reporting has also become much faster. Previously, you may have had to wait weeks, or potentially months, to get access to a new data source so you could do your analyses. Now, you can solve that yourself in a couple of hours, without having to wait for other people.

Looker gives companies a single source of truth for all their data.

“With Looker, it was about getting report building out of analytics, so analysts can spend their time actually forming opinions, defining strategy, doing analyses, and digging into the data, rather than just building reports day in and day out.”

– Ben Fischer, Sr. Director of Business Intelligence

“It also makes iteration much faster. We can define something, put it into production, and report off of it. If three days later we realize we forgot something, it’s a two-minute fix rather than going back to engineering, having someone spend half a day on it.” said Ben.

Finally, having access to Etleap allows the team to easily look at data from different angles, making the analysis and insights for clients even more valuable. “Etleap has a function for modeling data, which is useful for reporting, as it allows you to build the aggregations you need to power impactful reports. We can have processes that run everyday and get a quick summary of the data from all different perspectives. Before, it would have taken an engineer a couple of days to build that.” said Ben.

With Etleap and Looker, the AXS team finally has the time and resources to focus on bigger initiatives, including GDPR, internationalization, increasing accessibility across the organization, and providing even more data services to clients. With these tools in their arsenal, the sky is truly the limit.

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.

 

retainhistorywizard

 

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!