Remove Consulting Remove Data Integration Remove Data Lake Remove Data Quality
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Fire Your Super-Smart Data Consultants with DataOps

DataKitchen

When internal resources fall short, companies outsource data engineering and analytics. There’s no shortage of consultants who will promise to manage the end-to-end lifecycle of data from integration to transformation to visualization. . The challenge is that data engineering and analytics are incredibly complex.

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AWS Glue Data Quality is Generally Available

AWS Big Data

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning. It takes days for data engineers to identify and implement data quality rules.

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Avoid generative AI malaise to innovate and build business value

CIO Business Intelligence

Sixty-six percent of C-level executives are ambivalent or dissatisfied with the progress of their AI or GenAI efforts, according to Boston Consulting Group 1. GenAI requires high-quality data. Ensure that data is cleansed, consistent, and centrally stored, ideally in a data lake.

Data Lake 142
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An AI Chat Bot Wrote This Blog Post …

DataKitchen

Observability in DataOps refers to the ability to monitor and understand the performance and behavior of data-related systems and processes, and to use that information to improve the quality and speed of data-driven decision making. By using DataOps, organizations can improve. Query> When do DataOps?

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

Graphs reconcile such data continuously crawled from diverse sources to support interactive queries and provide a graphic representation or model of the elements within supply chain, aiding in pathfinding and the ability to semantically enrich complex machine learning (ML) algorithms and decision making.

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Fabrics, Meshes & Stacks, oh my! Q&A with Sanjeev Mohan

Alation

Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Everybody’s trying to solve this same problem (of leveraging mountains of data), but they’re going about it in slightly different ways.

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Modeling, Modernization and Automation

BI-Survey

While most continue to struggle with data quality issues and cumbersome manual processes, best-in-class companies are making improvements with commercial automation tools. The data vault has strong adherents among best-in-class companies, even though its usage lags the alternative approaches of third-normal-form and star schema.