Team productivity is hampered by data integration projects that require a significant amount of grunt work. But by automating these projects, teams free up time and resources to work on more important tasks.
Learn how to automate such data projects in our new eBook, 5 Data Integration Projects You Can Automate Right Now. Here’s an excerpt – download the free eBook for more!
#2 Data Framework Optimizations
A data framework refers to the infrastructure and processes that interact with data as it travels through a workflow. This can include not only code, but also physical components such as servers. Data framework optimizations can lead to improved performance and cost savings.
With ELT and Logic Rivers, teams can automate data framework optimizations. But first, a quick refresher. For decades, data integration solutions were modeled on ETL (extract, transform, load).
But in the past few years, ELT (extract, load, transform) solutions have emerged as a key alternative.
By removing the need for a secondary server, ELT inherently optimized many data frameworks. With ELT, data transformation is performed inside a cloud data warehouse, leading to faster, cheaper, and more efficient framework flows. But now that the technology is more widespread, teams that want a competitive edge should consider building on ELT’s optimizations using automation.
That’s where Logic Rivers come in.
A Logic River automatically orchestrates and transforms an entire data workflow, from start to finish. Logic Rivers allow teams to automate both the ingestion of data into the cloud and in-database transformations in the same workflow. With Logic Rivers, teams can automate core data framework optimizations. A perfect example of this appeared in our recent Case Study of AB Inbev:
“AB Inbev’s data stack initially included Hadoop, Azure Data Factory, Azure Database, and Azure Synapse Analytics. To eliminate Hadoop, Azure Database, and Azure Data Factory, the team automated both the ingestion of data (orchestration) and the execution of SQL queries (transformation) inside Azure Synapse Analytics using a Logic River.”
AB Inbev eliminated three of four solutions by automating this data framework optimization.
#3 Cloud Data Migration
With cloud data migrations, teams must migrate enormous amounts of on-premise data into a cloud data warehouse. For such a sprawling project, there are often many time-consuming and resource-draining tasks.
These tasks include mapping and source management, creating a data pipeline for each table, and tracking changes across all data sources, among many others. However, there are a number of ways to eliminate inefficiencies by using automation, such as:
- Choosing ELT over ETL – While ETL requires data to pass through a secondary server, ELT automates loading raw data directly into a cloud data warehouse. By bypassing the secondary server, ELT speeds up the data migration process significantly.
- Eliminating technical grunt work – Teams can automate a significant amount of technical grunt work by adopting a fully managed, no-code, auto-scalable data integration platform, along with pre-built data connectors and data process automation.
- Confirming data quality – Data quality solutions offer data verification between source and target. These solutions automate integrity checks throughout the migration process, alerts for data discrepancies, and validation across all data.
- Maintaining data continuity – A continuous migration automates data congruence between your cloud data warehouse and your database. This enables you to manipulate and analyze up-to-date data in the cloud.
Also, while many cloud migration solutions emphasize automation, sometimes these capabilities are more spin than reality. Consult objective review sites with real experts to make a decision, such as TrustRadius, G2, Capterra, SoftwareReviews, and IT Central Station.
Download 5 Data Integration Projects You Can Automate Right Now for more automation insights!