Pipeline management is reliable, easy, and flexible with Etleap ETL.
Data Ops: The Etleap Way
Etleap is built to handle the expected and the unexpected, preempting known issues and minimizing impact when new problems arise.
At each stage of the data ops workflow, Etleap ETL provides easy setup and guides users to clear issue resolution.
Etleap ETL has the flexibility to meet every customer’s dynamic data environment and unique needs.
Ready for the expected and the unexpected
Built with hundreds of thousands of pipelines of experience
- Billion-record Salesforce object extraction
- Unexpected schema change
- Ingesting millions of small files from an S3 bucket
- Redshift cluster being resized
- AWS EC2 instances being retired
- Expected a value to be an integer, was a string instead
- Temporary connection loss to on-premises Oracle database
- MS SQL database offline for backup
- Retroactive attribution of data in Google Ads
- Backfill of webhook events
- Errors raised while executing a custom Python function
- Changed order of columns in a CSV file
- Automatic scaling to handle terabytes of file data backfill
- Strings too wide for a Redshift varchar column
- Out-of-date data in the source
- Transformation SQL no longer valid
Managing data pipelines is a frustrating endeavor. Between infrastructure scaling, API details, source changes, and more, there are countless causes of pipeline failure. Manually writing code to preempt these known issues is a huge, error-prone undertaking.
Etleap ETL customers, on the other hand, increase pipeline uptime and reduce their dependence on internal, specialized ETL developers.
Etleap ETL has automatic detection and guided solutions for these known pipeline challenges, and Etleap’s engineering team constantly incorporates new ones as well.
Architected for the unknown
What about the corner cases? With the large variety of data sources and dynamic data volumes and velocity, it’s often issues that have never been seen before that cause everyday pipeline failures. Etleap ETL minimizes the impact of potential issues, detects them quickly when they do occur, and then mitigates them efficiently.
Etleap ETL's architecture is built with pipeline independence - an issue with one pipeline doesn't affect the operation of another. Examples include separate runtime environments per pipeline and automatic compute scaling in response to increased load of a single pipeline.
In the era of continuous rather than daily ETL, pipeline monitoring requires more sophistication. For instance, different approaches are needed to detect issues with a low-latency Kafka stream than a periodic pull from Google Analytics’ API. Etleap ETL’s monitoring is built to detect issues as soon as they occur and minimize false positives.
Tons of data pipeline instrumentation helps Etleap's team get to the bottom of issues quickly. Perhaps delays are caused by contention from an external process in the destination warehouse or maybe errors are caused by an unannounced API change. Etleap engineers can quickly diagnose these and resolve them accordingly.
Effortless Data Ops
An easier path to monitoring, alerting, and issue resolution
Like many ops functions, Data Ops can be framed into three distinct components: Monitoring, Alerting, and Resolution. These steps can quickly consume the time of multiple data engineers as the volume and complexity of data sources and pipelines increase.
Etleap ETL makes it easy to create pipelines and, just as importantly, it boosts Data Ops productivity. Monitoring and alerting are robust and configurable to meet each customer’s needs. Fast resolution of a single pipeline issue can eliminate hours or even days of engineering effort. Etleap automates many solutions and delivers easy-to-follow guidance when user action is needed.
Data Ops that fits each customer’s unique needs
While automation delivers much of the value behind Etleap ETL Data Ops, that does not make it a rigid black box. Etleap ETL is configurable, extensible, and adaptable to match customers’ environments.
Customers can configure Etleap ETL to manage how much manual intervention they want in the Data Ops workflow. For example, users can engage with every parsing error and make case-by-case decisions on schema changes. Alternatively, they can raise the error threshold for alerting and choose rules for handling schema changes.
Customers’ data pipelines often connect with separate processes and teams. Etleap ETL provides extensibility through APIs and events to support use cases like user-defined quality checks and CI/CD workflows.
Etleap ETL is built to adapt to the dynamic conditions of modern data environments, where changing data structures and volumes require data destinations and infrastructure to adjust seamlessly.
Customer Case Studies
We’re a small data team at Sauce, but there’s a team at Etleap that I can interact with and get stuff done. We haven’t had to hire more data engineers to do the work of maintaining pipelines.
Data Engineering Manager, Sauce Labs
Just as we’ve moved from on-prem to AWS because we don’t want to be in the business of building data centers, we’re leveraging Etleap because we don’t want to be in the business of building data pipelines.
Chief Data Officer, Morningstar
See why modern data teams choose Etleap
You’re only moments away from a better way of doing ETL. We have expert, hands-on data engineers at the ready, 30-day free trials, and the best data pipelines in town, so what are you waiting for?