The Ascend Blog

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

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

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

Learn effective metadata management practices to streamline your data engineering workflows, enhance data quality, & optimize your pipelines.

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

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

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

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

Explore how data products can deliver better value from data to end users in more useful and approachable ways.

Explore how to build a data pipeline in 6 steps, from design to deployment, and learn a new framework to simplify the process.

Uncover data orchestration's impact on workflow efficiency and reevaluate the necessity of an orchestration layer in your data strategy

Although every company has unique data challenges, there are several near-universal data pipeline best practices that can guide every data leader in building a solid foundation with their team.

Explore data pipeline orchestration, its strategic role in data management, and how it differs from general data orchestration.

Learn why Python for data engineering is the first choice for experts, its benefits, and how it's used in real-world applications.

Discover why Zero ETL might not live up to the hype. Exploring its rise, benefits, and deeper implications in the data landscape.

Explore Snowflake Snowpark: what it is, its breakthrough features, impact on developers, and optimization tips.

Explore the evolution from ETL and ELT to the dynamic EtLT approach, highlighting the shift towards continuous data refinement in modern data operations.

Learn what end-to-end data pipelines are and why they're crucial for data teams aiming for efficiency and success.

Explore the role of a Data Automation Engineer: mastering automation to streamline data pipelines and drive high-impact business results.

Explore why Notebooks, while powerful for prototyping, may not be ideal for production data pipelines and discover robust alternatives.

Discover the top 3 data mesh challenges and effective strategies to overcome them for successful data mesh implementation.