Remove Data Architecture Remove Data Transformation Remove Data Warehouse Remove Visualization
article thumbnail

How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

To speed up the self-service analytics and foster innovation based on data, a solution was needed to provide ways to allow any team to create data products on their own in a decentralized manner. To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse.

article thumbnail

BMW Cloud Efficiency Analytics powered by Amazon QuickSight and Amazon Athena

AWS Big Data

They can use their own toolsets or rely on provided blueprints to ingest the data from source systems. Once released, consumers use datasets from different providers for analysis, machine learning (ML) workloads, and visualization. The difference lies in when and where data transformation takes place.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Build incremental data pipelines to load transactional data changes using AWS DMS, Delta 2.0, and Amazon EMR Serverless

AWS Big Data

Data ingestion – Steps 1 and 2 use AWS DMS, which connects to the source database and moves full and incremental data (CDC) to Amazon S3 in Parquet format. Data transformation – Steps 3 and 4 represent an EMR Serverless Spark application (Amazon EMR 6.9 Monjumi Sarma is a Data Lab Solutions Architect at AWS.

article thumbnail

Birst automates the creation of data warehouses in Snowflake

Birst BI

Managing large-scale data warehouse systems has been known to be very administrative, costly, and lead to analytic silos. The good news is that Snowflake, the cloud data platform, lowers costs and administrative overhead. What gaps does the joint solution address in the market?

article thumbnail

What Is Embedded Analytics?

Jet Global

This is in contrast to traditional BI, which extracts insight from data outside of the app. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Plus, there is an expectation that tools be visually appealing to boot. Their dashboards were visually stunning.