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DuckLake's SQL-first lakehouse architecture works with Ascend's agentic data engineering platform to deliver faster, simpler data pipelines than traditional Iceberg and Delta Lake setups.

Learn how MCP servers enable intelligent agents to automate data pipeline operations, from GitHub issues to Slack alerts. Reduce manual toil with agentic automation.

Learn how DataOps agents automate the manual work that consumes 50% of data engineers' time, from pipeline failures to code reviews.

New AI-powered agents seamlessly integrate with existing workflows to automate incident response, code reviews, pipeline optimization, and more.

Build an automated release notes pipeline using GitHub Actions and OpenAI.

Summary: Ascend.io introduces Agentic Data Engineering, a new platform that shifts routine data pipeline tasks to embedded intelligent agents. This enables teams to build faster, reduce errors, and spend more time solving business-critical problems.

Discover Agentic Data Engineering: The first AI-native data stack that deploys intelligent agents to build, manage, & optimize your data pipelines.

As we all navigate these uncertain times, one thing has never been more certain — the need to reduce spend. Dollars saved directly equates to employees kept, strategic investments continued, and savings passed on to customers. Finding unnecessary spend and inefficiencies across the business is more important than ever before.

This tutorial, the first in a three-part series on Data Lake ETL (Extract, Transform, Load) will give you a brief overview on how to quickly and easily you can accomplish the “E” of ETL to ingest data from any data source with Ascend.

Unifying Fragmented Data Ecosystems, Ascend.io Offers New Flexible Coding Options to Build Custom Data Connectors and Support for 40+ Connector Types

Now in Ascend, all stages of all Dataflows are queryable without switching tools or disrupting your development process. As part of this capability, we’ve built an interactive query editor that lets you interact with Connectors and Transforms in Dataflows, as well as Data Feeds broadcasting from other Data Services, as though they are read-only tables in a SQL database.

At Ascend, we are excited to introduce a new paradigm for managing the development lifecycle of data products — Declarative Pipeline Workflows. Keying off the movement toward declarative descriptions in the DevOps community, and leveraging Ascend’s Dataflow Control Plane, Declarative Pipeline Workflows are a powerful tool that allows data engineers to develop, test, and productionize data pipelines with an agility and stability that has so far been lacking in the DataOps world.

Harry's head of analytics, Pooja Modi, and data analyst, William Knighting, spearheaded a new initiative to find a more powerful solution to expedite ingesting, transforming, and delivering data feeds to Looker in a manner that correlates brand information across functions. With these criteria in mind, the Harry’s data team found The Ascend Unified Data Engineering Platform.

This week, the team at Ascend is launching our Autonomous Dataflow Service, which enables data engineers to build, scale, and operate continuously optimized, Apache Spark-based pipelines.

Credentials Vault is a centralized place to store and manage secrets used by your dataflows. The feature makes it even easier to collaborate with others to quickly ingest from, and write to external data systems. It also empowers site administrators with an interface to audit and control all credentials in use by the Ascend platform.

This tutorial will give you an overview of the “T” in ETL, namely, how to start transforming your data before you load it into the final destination. We will use SQL in this example, but Ascend also supports Python/PySpark and Scala/Java transformations as well.

Now that we’ve extracted some data from S3, cleaned it up using a SQL transform, we can start on the “L” of ETL and write our data back out to our data lake. Follow this guide to learn how.

Learn 5 essential data engineering best practices to improve pipeline reliability, scalability, and team efficiency with insights from Ascend.io experts.

While your data team likely includes people with all three of these points of view, what really matters is the position of the leaders, and the pace with which the team is adapting to the real needs of the business. So while we’re rolling the dice with the alphas, let’s take a moment to look at the two sources of value in this context: data and code.

Let’s look at how managers of data teams can set the stage for a path that fuels speed and business results, by sorting out different aspects of what needs to get accomplished, and tagging four common mistakes as killer anti-patterns.

It is a lesson that Sean Knapp, Founder and CEO at Ascend lives by. I met Sean at his Palo Alto office a few weeks ago, and as we chatted about the state of the market and how industries are shifting towards “as a Service” in almost every aspect, we found ourselves on a track about what really makes a startup successful.

This How-to will provide you with an overview of how to ingest data into Snowflake by building a production pipeline that automatically keeps data up to date, retries failures, and notifies upon any irrecoverable issues.

We are excited to announce the release of our new Python SDK! This SDK sits on top of Ascend’s public API, and is dynamically generated based upon the Protocol Buffer and gRPC (which we use extensively) definitions of all components within the Ascend platform.

Data scientists and data engineers play the primary role in accelerating a company’s data sophistication, providing both the technology and domain expertise from a sea of zeros and ones into valuable data products. As we reflect on 2019 and look forward to the year and decade ahead, we will examine the evolving nature of these roles and teams.

As the demand for data engineers increases, data teams must be prepared with the right tools and resources to ensure the role maintains a productive and effective workflow

Discover CI/CD practices. Learn how automation, testing, and version control improve data pipeline reliability and efficiency.

Explore the top data engineering trends shaping 2025. Learn how to future-proof your team with smarter workflows and automation.

Discover essential strategies for modernizing your data management approach in the AI era from industry experts. Learn how to transform traditional systems into AI-ready infrastructures, overcome common implementation challenges, and leverage data governance best practices to maximize your AI investments while maintaining security and compliance.

We are thrilled to announce that Ascend.io has been recognized as one of Built In's Best Places to Work for 2025 in the San Francisco Bay Area!

Data engineering is evolving rapidly, driven by advancements in AI and automation. As teams face increasing demands, the need for efficient and effective solutions has never been greater.

Data teams, here's your checklist to prep for the new year. Discover key strategies to enhance productivity and stay ahead with AI, Automation, DataOps, and more.

Learn how to build resilient, scalable data pipelines that adapt to evolving tech and business needs with automation and intelligence.

Explore how AI is reshaping data engineering roles, skills, and career paths. Learn how Ascend.io supports engineers in this evolving landscape.

A Guide to Data Transformation Techniques: Discover essential methods for transforming data from basics to advanced to create more value from your data.

Data flows are crucial for seamless integration and data management. Discover how to design your data flows in 6 key steps.

Leverage AI to enhance data quality and reliability. Discover AI-driven solutions for enhanced data integrity.

At Ascend, we believe it's time to rethink data engineering from the ground up. As the world of data continues to evolve at a breakneck pace, we are thrilled to announce the next revolutionary step in our journey — Project Inception.

Metadata is the information that provides context and meaning to data, ensuring it's easily discoverable, organized, and actionable. It enhances data quality, governance, and automation, transforming raw data into valuable insights.

What is DataOps? Discover benefits, best practices, and implementation strategies to enhance your data workflows and drive business outcomes.

Explore the relationship between AI and data engineering: How do they impact each other, and what does the future of their collaboration look like?

Snowflake Summit 2024 has set the stage for exciting changes in the data landscape. As a data enthusiast and a leader in data engineering, I'm eager to share my reflections on these innovations and their implications for Ascend.

Explore the complexities of data replication strategies in a way that's easy to understand, including types and how to choose the right one.

Unlock AI's potential with the right data platform: Explore AI data platform requirements, cloud capabilities, and automation for innovation.

Strategic partnership between Wizeline and Ascend.io to deliver significant enhancements in efficiency, security, and modernization with advanced AI technology solutions & services.

Explore the framework to set up a robust AI ecosystem and the key considerations for an effective AI implementation strategy.