What is Attunity Compose?
Attunity Compose simplifies all aspects of data lake and data warehouse design, development, data loading, deployment and updates by automating the most manual, mundane and repetitive tasks.
Attunity Compose for Data Lakes
Attunity Compose for Data Lakes automates data lake pipelines to create analytics-ready data sets, thereby helping organizations realize faster time-to-value for their data lake investments. Data architects leverage Attunity Compose for Data Lakes to automate data ingestion, data lake schema creation, data set provisioning and streamed data updates.
Attunity Compose for Data Lakes standardizes and combines change streams into a single transformation-ready data store in the lake. It automatically merges the multi-table and/or multi-sourced data into a flexible format and structure while retaining full history. The resulting persisted data history provides data consumers with rapid access to trusted data, including full lineage, without needing to understand the automated structuring that has taken place.
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The following archival documentation contains legacy content for older software release versions that are no longer supported. This information is provided for reference purposes only.
Attunity Compose for Data Warehouses
Attunity Compose for Data Warehouses automates the design, deployment, and operation of agile data warehouses. A boon for data architects, Attunity Compose for Data Warehouses accelerates the time-to-value of data warehouse projects by reducing the time and resources necessary to implement a data warehouse, data mart, or data hub.
Attunity Compose for Data Warehouses virtually eliminates manual, time-consuming, and error prone data warehouse tasks by automating data modeling, ETL generation and production workflow. In addition, Attunity Compose for Data Warehouses reduces the time and cost of analytics projects based on cloud platforms such as Amazon Redshift, Azure SQL Data Warehouse and Snowflake. Users can quickly spin up, load, and iterate their data warehouses, by dynamically adjusting data sources and models in response to changing business requirements.