The processing of big data is a critical task for every organisation in today's data-driven world. Data engineers need services built to simplify ETL in order to unlock transformative insights. As well as managing the complexities of big data integration and scale challenges around it.
Data generated by a variety of product applications are growing exponentially on a daily basis. Since the data comes from many sources, it is very hard to handle it. To analyze and store all this data in the right order, we use Azure Data Factory which:
Collect and stores data with the help of Azure Data Lake Storage Analyze the data Using pipelines to transform the data (a logical grouping of activities that perform a task together) Publish structured data Visualizes data with third-party applications such as Apache Spark or Hadoop.
With Azure Data Factory, it becomes fast and easy to build code-free or code-centric ETL and ELT processes. Here in this e-book, we present the case study to share crucial insights on how our client was very pleased with the ADF and Azure SQL Data Warehouse solution and how the solution costs a fraction of what it did previously whilst keeping it all inside the client’s Azure environment. Reach Out To Find Out How We Can Help Your Needs For Data Science.