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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? Keep it agile, with short design, develop, test, release, and feedback cycles: keep it lean, and build on incremental changes. Test early and often. Test and refine the chatbot.

Strategy 290
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Apache Iceberg optimization: Solving the small files problem in Amazon EMR

AWS Big Data

Iceberg tables store metadata in manifest files. As the number of data files increase, the amount of metadata stored in these manifest files also increases, leading to longer query planning time. The query runtime also increases because it’s proportional to the number of data or metadata file read operations. with Spark 3.3.2,

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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

Offering this service reduced BMS’s operational maintenance and cost, and offered flexibility to business users to perform ETL jobs with ease. Manually upgrading, testing, and deploying over 5,000 jobs every few quarters was time consuming, error prone, costly, and not sustainable.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Apache Iceberg is designed to support these features on cost-effective petabyte-scale data lakes on Amazon S3. The snapshot points to the manifest list.

Data Lake 118
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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

Paired to this, it can also: Improved decision-making process: From customer relationship management, to supply chain management , to enterprise resource planning, the benefits of effective DQM can have a ripple impact on an organization’s performance. Your Chance: Want to test a professional analytics software? 1 – The people.

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Modernize your data observability with Amazon OpenSearch Service zero-ETL integration with Amazon S3

AWS Big Data

The new integration with OpenSearch Service supports AWS’s zero-ETL vision to reduce the operational complexity of duplicating data or managing multiple analytics tools by enabling you to directly query your operational data, reducing costs and time to action. Let’s dig into this exciting new feature for OpenSearch Service.

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Accelerate HiveQL with Oozie to Spark SQL migration on Amazon EMR

AWS Big Data

Customers now want to migrate their Apache Hive workloads to Apache Spark in the cloud to get the benefits of optimized runtime, cost reduction through transient clusters, better scalability by decoupling the storage and compute, and flexibility. The script generates a metadata JSON file for each step.