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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Data governance in the age of generative AI

AWS Big Data

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications.

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DataOps with Matillion and DataKitchen

DataKitchen

The Matillion data integration and transformation platform enables enterprises to perform advanced analytics and business intelligence using cross-cloud platform-as-a-service offerings such as Snowflake. DataKitchen acts as a process hub that unifies tools and pipelines across teams, tools and data centers.

Testing 130
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Accelerate analytics on Amazon OpenSearch Service with AWS Glue through its native connector

AWS Big Data

As the volume and complexity of analytics workloads continue to grow, customers are looking for more efficient and cost-effective ways to ingest and analyse data. OpenSearch Service is used for multiple purposes, such as observability, search analytics, consolidation, cost savings, compliance, and integration.

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Five benefits of a data catalog

IBM Big Data Hub

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Ensuring data quality is made easier as a result.

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Power of ETL: Transforming Business Decision Making with Data Insights

Smart Data Collective

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. By understanding the power of ETL, organisations can harness the potential of their data and gain valuable insights that drive informed choices. Both approaches aim to improve data quality and enable accurate analysis.

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The DataOps Vendor Landscape, 2021

DataKitchen

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. OwlDQ — Predictive data quality.

Testing 307