Ascend.ioThe Data Engineering Company
Data Engineering, Evolved
For years, we’ve focused on creating systems that store more data, and process more data, than ever before. As we built more and more systems, we found the missing piece of technology was the system that helped us build more than ever before.
As Data Engineers, we love building pipelines. What we don’t love is:
- How long it takes to implement the simple things
- How brittle pipelines can be
- Edge cases, parameter tuning, backfills, and more
Our goals in creating the Ascend Unified Data Engineering Platform:
- 10x faster build velocity
- Automated maintenance
- Smarts to do the rest (aka, “DataAware™ intelligence”)
As our world becomes more data-driven, data teams must have more intelligent, autonomous platforms to keep them moving faster than the volume of data itself. We are working on building our Unified Data Engineering Platform to solve just that. We help data teams build faster and maintain less, so that they can spend more time driving innovation for their company.
Board Member at Visa & Salesforce, Former COO at Ebay
CTO at Microsoft
Former CEO at Sun Microsystems
Former CMO at Confluent, VP Marketing at Pure Storage
Managing Partner at Softbank, SVP Product at LinkedIn
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This TDWI Best Practices Report examines experiences, practices, and technology trends that focus on identifying bottlenecks and latencies in the data’s life cycle, from sourcing and collection to delivery to users, applications, and AI programs for analysis,...
Modern data pipelines run on some of today’s most advanced technologies, yet the process of building, scaling, and maintaining them is as challenging as ever. This is a familiar pattern found across the tech industry, as the innovation focus shifts from raw...
At Ascend we see all sorts of different pipelines. One pattern we see quite often is that of change data capture (“CDC”) from databases and data warehouses, followed by data set reconstitution. Doing this data set reconstitution usually requires a full reduction — a transform in which you iterate over all records to find those representative of the latest state. This can become inefficient over time, however, as greater and greater percentages of any given data set become “stale”.