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Don’t let your data pipeline slow to a trickle of low-quality data

IBM Big Data Hub

Businesses of all sizes, in all industries are facing a data quality problem. 73% of business executives are unhappy with data quality and 61% of organizations are unable to harness data to create a sustained competitive advantage 1. Data observability as part of a data fabric . Instead, Databand.ai

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

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How Amazon Devices scaled and optimized real-time demand and supply forecasts using serverless analytics

AWS Big Data

We also used AWS Lambda for data processing. To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. Clients access this data store with an API’s.

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“You Complete Me,” said Data Lineage to DataOps Observability.

DataKitchen

It allows organizations to see how data is being used, where it is coming from, its quality, and how it is being transformed. DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data lineage is static and often lags by weeks or months.

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AWS Glue streaming application to process Amazon MSK data using AWS Glue Schema Registry

AWS Big Data

Acting as a bridge between producer and consumer apps, it enforces the schema, reduces the data footprint in transit, and safeguards against malformed data. AWS Glue is an ideal solution for running stream consumer applications, discovering, extracting, transforming, loading, and integrating data from multiple sources.

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Cloud Data Warehouse Migration 101: Expert Tips

Alation

“Cloud data warehouses can provide a lot of upfront agility, especially with serverless databases,” says former CIO and author Isaac Sacolick. There are tools to replicate and snapshot data, plus tools to scale and improve performance.” Data quality /wrangling. Ability to move out/costs of data egress.