Our Partners

 

Ascend capabilities are designed from the ground up to integrate seamlessly with other enterprise-grade platforms and cloud services. Especially our partner ecosystem consists of complimentary solutions and services that are pre-tested and with whom we maintain ongoing dialog to jointly solve customer problems.

 

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Cloud Partners

Ascend runs natively on Google Cloud, Microsoft Azure, and AWS, either as SaaS or as a fully managed service in VPCs.   Each customer can use the built-in connector library to link Ascend up to their on-premise data centers.

Ascend runs natively on Microsoft Azure, and integrates easily with Azure-based data sources, solutions, and partners. Ascend connectors also bridge into other clouds and seamlessly operationalize pipeline dataflows across cloud boundaries. You can find Ascend on the Azure Marketplace.

Ascend runs natively on Google Cloud, and integrates easily with Google Cloud-based data sources, solutions, and partners, and will soon be available in the Google Cloud marketplace. Ascend connectors also bridge into other clouds and seamlessly operationalize pipeline dataflows across cloud boundaries. You can find Ascend among the Google Cloud Technology Partners.

Ascend runs natively on AWS, and integrates easily with AWS-based data sources, solutions, and partners. Ascend connectors also bridge into other clouds and seamlessly operationalize pipeline dataflows across cloud boundaries. You can find Ascend on the AWS Marketplace.

Technology Partners

Ascend continues to integrate our unified data engineering platform with other technologies out-of-the-box, which together can meet overall enterprise business and data needs.  If you have a technology solution or cloud service that augments Ascend’s unified data engineering platform, please contact us to discuss potential partnership and integration.  

Ascend provides native integration with Snowflake. Ascend pipelines can source data from Snowflake data stores, invoke Snowflake data anywhere in the pipeline flow, and terminate pipelines seamlessly in Snowflake as well. As a result, data engineers can use Ascend to consolidate their relational and big data processing strategies, and reduce seams and integration to their business intelligence platforms.

Ascend provides connections to Looker, a unified data platform that delivers actionable business insights to employees at the point of decision. Looker integrates data into the daily workflows of users to allow organizations to extract value from data at web scale, including big data pipelines running on Ascend. Over 2000 industry-leading and innovative companies such as Sony, Amazon, The Economist, IBM, Etsy, Lyft and Kickstarter have trusted Looker to power their data-driven cultures.

Ascend provide native integration with Tableau, so that data engineers can integrate their Tableau datastores directly to Ascend data pipelines. Data analyst users can use Tableau to directly render the output of operational data products running on Ascend in near-real time.

PowerBI integrates seamlessly with Azure-based Ascend data pipelines, so that users can render the output of operational data products running on Ascend with PowerBI in near-real time.

By connecting Ascend with Thoughtspot, non-technical business users can query the output of live Ascend pipelines through ThoughtSpot’s search-driven analytics to answer questions they know to ask. Meanwhile, the SpotIQ AI-driven analytics engine sifts through Ascend output to automatically answer thousands of questions a user would care about but wouldn’t even know to ask.

“Works With” Technologies

Ascend is committed to open source in a number of ways. First, Ascend relies on open source infrastructure ranging from Kubernetes to Spark.  Second, Ascend includes open source environments like Python. Third, Ascend supports interoperability with open source platforms like kafka, Zeppelin, and Jupyter.  This list is not exhaustive - if you are using open source in your data technology stack and are curious about how Ascend would work with it, please contact us.

Ascend delivers finished data products at the end of the pipelines to be queried directly into Jupyter notebooks. Jupyter notebooks can also be injected at any point in the data pipeline via PySpark, so that Jupyter models can act as a transform and inject their output directly back into the pipeline graph for further processing.

Ascend includes native connectors to receive kafka topics and ingest kafka data streams in real-time anywhere along autonomous Ascend data pipelines. Ascend immediately and autonomously updates all dependent transforms and data products.

Ascend delivers finished data products at the end of data pipelines to be queried directly into Zeppelin notebooks. Zeppelin notebooks can also be injected at any point in the data pipeline via PySpark, so that Zeppelin models can act as a transform and inject their output directly back into the pipeline graph for further processing.

Ascend operates and optimizes pipelines on Apache Spark with minimal coding. Pipeline transformations are designed using Spark SQL and pySpark. Ascend provides data engineers full configurability and visibility into the autonomous Spark jobs generated and operated by the platform on their behalf.

Developers can use python within Ascend, as well as use Ascend APIs from their python apps. Python is used to quickly build and deploy connectors to data sources, orchestrate data pipelines in the context of business apps and ML workflows, consume the output of Ascend data pipelines, as well as many other applications.

Ascend includes connectors to MySQL databases. Enterprises can easily connect to, discover, navigate, choose, and select database schemas and content to ingest anywhere along their Ascend pipelines, as well as seamlessly deliver data pipeline output into MySQL databases.

Ascend includes connectors to PostgreSQL data warehouses. Enterprises can easily connect to, discover, navigate, choose, and select data warehouse content to ingest anywhere along their Ascend pipelines, as well as deliver data pipeline output into PostgreSQL data warehouses.

Ascend transforms are designed in SparkSQL and pySpark, including pySpark ML libraries such as Jupyter. Data engineers can quickly migrate older data pipelines written with PySpark by easily extracting the ~5-10% of actual data logic from the rest of the old code and inserting it into Ascend transforms.

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