At Acryl Data, we are passionate about revolutionizing data management and empowering organizations to regain control of their fragmented data stacks. We do this first and foremost by driving the open source DataHub Project and collaborating with the largest metadata community in the world.
Enter your email to get Acryl News directly to your inbox:
Humans of DataHub
Modern Data Stack
Tech Deep Dive
Open Source Software
When organizations struggle to operationalize ML or AI solutions, the root causes are usually data-related. ML and AI teams can’t find the data they need to define use cases, engineer features, or train their models. When they can find it, they can’t always use it—because they don’t know what it is, where it came from, who created it, when, or for what purpose. Lacking context, any dataset is a black box. Discover why a modern data catalog and metadata platform is a foundational element of any ML or AI platform.
Increasingly, decision-makers and stakeholders just don’t trust their data and analytics—usually because what they’re seeing is out-of-date, incomplete, inconsistent, and sometimes flat-out wrong.
Data work is a true team sport. Each and every data asset is the product of a clear distribution of labor, with people in a diversity of roles—including data practitioners, software developers, architects, governance authorities, and business domain experts—working collaboratively.
You're a data engineer at a boutique e-commerce start-up. Your company sells luxury goods at steep discounts. One of your many responsibilities involves monitoring the "flash_sale_purchase_events" table in your start-up’s Snowflake data warehouse. Updates to columns in this table are supposed to reflect real-time participation by customers in the limited-time flash sales your company offers.
We built a SQL lineage parser that's schema-aware and can generate accurate column-level lineage from SQL queries. In our tests, it works significantly better than other open-source, Python-based lineage tools.
Some partnering announcements are especially sweet—like this one.
Are you ready to dive into the world of open source and make a meaningful contribution? Hacktoberfest 2023 is here, and we're thrilled to invite you to participate by contributing to the DataHub project.
Learn about Volume Assertions, Acryl Observe’s solution for detecting data volume issues for tables on Snowflake, BigQuery, or Redshift, and the pivotal role it can play in keeping your data healthy.
If you're part of a data team responsible for a business-critical dataset, dashboard, or any other data asset, you know how important it is to stay on top of any upstream changes before they impact you and your stakeholders. What if a table you rely on just got deprecated? What if a column you use was removed upstream? Or if an upstream table missed an update and now has stale, un-synced data? Staying updated on critical assets in real time is critical to effective data monitoring and data quality. Given the complexity of today’s data environment, doing this is no walk in the park. But what if there was a way to stay in the loop all the time? And know exactly what happened – right when it happened? With Acryl DataHub's Subscriptions and Notifications feature, you can.