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

AWS Big Data

One key component that plays a central role in modern data architectures is the data lake, which allows organizations to store and analyze large amounts of data in a cost-effective manner and run advanced analytics and machine learning (ML) at scale. To overcome these issues, Orca decided to build a data lake.

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Join a streaming data source with CDC data for real-time serverless data analytics using AWS Glue, AWS DMS, and Amazon DynamoDB

AWS Big Data

Customers have been using data warehousing solutions to perform their traditional analytics tasks. About the authors Manish Kola is a Data Lab Solutions Architect at AWS, where he works closely with customers across various industries to architect cloud-native solutions for their data analytics and AI needs.

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Data Observability and Monitoring with DataOps

DataKitchen

Data errors impact decision-making. When analytics and dashboards are inaccurate, business leaders may not be able to solve problems and pursue opportunities. Data errors infringe on work-life balance. Data errors also affect careers. Data sources must deliver error-free data on time.

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Patterns for updating Amazon OpenSearch Service index settings and mappings

AWS Big Data

Amazon OpenSearch Service is used for a broad set of use cases like real-time application monitoring, log analytics, and website search at scale. Use the reindex API operation The _reindex operation snapshots the index at the beginning of its run and performs processing on a snapshot to minimize impact on the source index.

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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

In this post, we share how the AWS Data Lab helped Tricentis to improve their software as a service (SaaS) Tricentis Analytics platform with insights powered by Amazon Redshift. Finally, data integrity is of paramount importance. For users that require a unified view of software quality, this is unacceptable.

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Break data silos and stream your CDC data with Amazon Redshift streaming and Amazon MSK

AWS Big Data

Traditionally, customers used batch-based approaches for data movement from operational systems to analytical systems. A batch-based approach can introduce latency in data movement and reduce the value of data for analytics. usually a data warehouse) needs to reflect those changes in near real-time.

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

DataKitchen

To capture a more complete picture of the data’s journey, it is important to have a DataOps Observability system in place. Data lineage is static and often lags by weeks or months. Data lineage is often considered static because it is typically based on snapshots of data and metadata taken at a specific time.

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