Ascend fits easily into your existing data ecosystem, whether you are just starting your pipeline journey, or have thousands already running. Quickly connect to any system to start building. Autonomous data pipelines directly fuel downstream applications, analytics, and machine learning models.
Data Engineering Unified
More Architecting, Less Plumbing.
The Ascend Unified Data Engineering Platform gives data teams 10x faster build velocity and automated maintenance for modern data pipelines. Generate autonomous data pipelines that dynamically adapt to any changes in data, code or environment. Evolve beyond traditional ETL and data orchestration tools to ingest, build, run, integrate, and govern advanced data pipelines with 95% less code.
Ascend’s DataAware™ intelligence observes and maintains detailed records of data movement and processing, code changes and user activity, enabling data pipelines to run at optimal efficiency with integrated lineage tracking, auditability and governance.
Deploy Ascend on top of existing Apache Spark clusters, or as a fully managed solution.
One platform to get from prototype to production
Tap into your data sources with little to no code from any data lake, warehouse, database, stream or API, simply by describing the inputs. Ascend automatically monitors for new data, format conversions, data profiling, and incremental processing.
Any Data, Anywhere, Any Format
Connect to any lake, queue, warehouse, database, or API. Choose from a large library of connectors, or create your own with just a few lines of code.
Automated Change Detection
Automated Data Profiling
Automatically profile every piece of data. Analyze the mins and maxes of every column, of every partition of data. See how values change over time, and keep an eye on things like data anomalies.
Automated Data Reformatting
Not all data comes in ready for big data processing. Whether it is too many small files that need to be aggregated, too few large files that should be partitioned, or simply GZIP’ed CSVs that should be converted to Snappy compressed Parquet files, we have you covered.
Declarative Data Pipelines
Design your pipelines with declarative definitions that require 95% less code and result in far less maintenance. Specify inputs, outputs, and data logic in SQL, Python, Scala, and Java specs.
Interactive Pipeline Builder
Treat any stage, of any data pipeline, as a queryable table. Quickly prototype new pipeline stages, or run ad-hoc queries against existing pipeline stages, all in a matter of seconds. Ascend’s DataAware will notify you when the underlying data has changed, and makes converting your queries to pipelines in just a few clicks.
Git & CICD Integration
Data & Job Deduplication
Automated Spark Management
Connect Jupyter, Zeppelin, and more directly to Ascend for fast, efficient access to data as it moves through your data pipelines.
BI & Data Visualization
Feed data directly from Ascend to your BI and Data Visualization tools via Ascend’s High Performance Records API.
Get direct access to Ascend’s internal storage (.snappy.parquet) files for efficient processing by other big data systems. Ascend’s File-based Access offers fully transaction reads across multiple files, and guarantees that the data available is always linked directly to an active data pipeline.
Record APIs & SDKs
Read records from any stage of any data pipeline via Ascend’s high throughput records API. Connect Ascend directly to your applications, BI, and Viz tools with one easy API.
Data Lineage Tracking
Resource & Cost Reporting
Activity Monitoring & Reporting
Secure Data Feeds
Data Garbage Collection
Talk to an Expert
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