Common Change Data Capture Usage Patterns
Dive deep into common Change Data Capture (CDC) techniques to efficiently track and understand database changes and their impacts.
Practices for Data Warehousing with Ascend—Automate the Data Warehouse as a Projection
In this second post in the series “Practices for Data Warehousing with Ascend”, we’ll explore the concept of automating the management of a data warehouse through the projection of source data.
Practices for Data Warehousing with Ascend—Early Query, Fast Iteration, Collaboration
Welcome to the first in a series of posts regarding patterns and practices we’ve learned while working with customers using Ascend within the core of their data platform/stack to help them build and manage data warehouses.
Operationalize Machine Learning for Marketing and Product Teams Using Ascend and Vidora
This How-to video will provide you with an overview of how you can seamlessly ingest change data capture (CDC) data into the Ascend Platform for easy ETLT.
How To: Ingest CDC Data Into the Ascend Platform
This How-to video will provide you with an overview of how you can seamlessly ingest change data capture (CDC) data into the Ascend Platform for easy ETLT.
How To: Ingest Kafka Event Data with Flex Code Data Connectors
This How-to video will provide you with an overview of how you can now quickly and easily ingest Kafka event data into the Ascend Unified Data Engineering Platform.
How To: Getting Started with Flex Code Data Connectors
This How-to video will provide you with an overview of the new Flex Code Data Connectors, including how to get started with your own connectors.
How To: Connecting Ascend.io to Power BI via ODBC
This how-to will walk through how to set up an ODBC connection between Ascend and Power BI in cases when your dataset is too large to expose through the web connector.
How-to: Redshift Data Ingest & ETL with Ascend.io
This How-to will provide you with an overview of how to ingest data into Redshift by building a production pipeline that automatically keeps data up to date, retries failures, and notifies upon any irrecoverable issues.
How-to: Snowflake Data Ingest & ETL with Ascend.io
This How-to will provide you with an overview of how to ingest data into Snowflake by building a production pipeline that automatically keeps data up to date, retries failures, and notifies upon any irrecoverable issues.
Data Lake ETL Tutorial: Transforming Data
This tutorial will give you an overview of the “T” in ETL, namely, how to start transforming your data before you load it into the final destination. We will use SQL in this example, but Ascend also supports Python/PySpark and Scala/Java transformations as well.
Data Lake ETL Tutorial: Using Ascend No- and Low-Code Connectors to Load Data
Now that we’ve extracted some data from S3, cleaned it up using a SQL transform, we can start on the “L” of ETL and write our data back out to our data lake. Follow this guide to learn how.
