Systems | Development | Analytics | API | Testing

Accelerate the extraction, loading, storing and transformation of data — including data from SAP systems — for faster, richer manufacturing insights.

Data ingestion is an important component of any successful data initiative. It refers to the process of collecting data from multiple sources and loading it into another system. Businesses most commonly use a subtype of data ingestion called ETL (extract, transform, load), which allows the data to be transformed before it's loaded. This extra step provides many benefits. Most importantly, it allows organizations to automatically match and correlate data from a variety of different sources.

Data is the biggest value driver for businesses bringing positive change and enabling near term efficiency.

Fivetran, Talend, and Integrate.io are three of the most popular ETL tools on the market. Each platform offers unique features and capabilities, but deciding which ETL tool best suits your business depends on your needs and requirements.

BigQuery change data capture lets you replicate, insert, update, and/or delete changes from source systems without DML MERGE-based ETL pipelines.

Amazon Redshift is a popular cloud-based data warehousing solution that allows users to store and analyze large amounts of data quickly and efficiently. However, configuring a Redshift cluster for optimal performance can be challenging.

Yellowfin has always been driven by the belief that Business Intelligence (BI) solutions should be accessible and useful for the average business user - without the need to rely on a data analyst, or to have data analysis skills.

AWS DMS (Amazon Web Services Database Migration Service) is a managed solution for migrating databases to AWS. It allows users to move data from various sources to cloud-based and on-premises data warehouses. However, users often encounter challenges when using AWS DMS for ongoing data replication and high-frequency change data capture (CDC) processes.

This blog looks at some of the risks associated with data mesh and why organizations need to look at more than just the concepts of distributed data management to ensure successful data mesh. Companies need to evaluate the needs for managing their data products, data governance, the use of data platforms, and how business domains will be managed across the data ecosystem.