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Data architecture strategy for data quality

IBM Journey to AI blog

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

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A Beginner’s Guide to Data Warehousing

Unite.AI

This article will explore data warehousing, its architecture types, key components, benefits, and challenges. What is Data Warehousing? Data warehousing is a data management system to support Business Intelligence (BI) operations. It can handle vast amounts of data and facilitate complex queries.

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Celebrating 40 years of Db2: Running the world’s mission critical workloads

IBM Journey to AI blog

Don Haderle, a retired IBM Fellow and considered to be the “father of Db2,” viewed 1988 as a seminal point in its development as D B2 version 2 proved it was viable for online transactional processing (OLTP)—the lifeblood of business computing at the time. Db2 (LUW) was born in 1993, and 2023 marks its 30th anniversary.

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Popular Data Transformation Tools: Importance and Best Practices

Pickl AI

Inconsistent or unstructured data can lead to faulty insights, so transformation helps standardise data, ensuring it aligns with the requirements of Analytics, Machine Learning , or Business Intelligence tools. This makes drawing actionable insights, spotting patterns, and making data-driven decisions easier.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning Blog

This includes implementing access controls, data governance policies, and proactive monitoring and alerting to make sure sensitive information is properly secured and monitored. For a more detailed description, see Scaling AI and Machine Learning Workloads with Ray on AWS and Build a RAG data ingestion pipeline for large scale ML workloads.

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Discover the Snowflake Architecture With All its Pros and Cons- NIX United

Mlearning.ai

Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. Therefore, quick data ingestion for instant use can be challenging.

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10 Best Data Engineering Books [Beginners to Advanced]

Pickl AI

Data Engineering plays a critical role in enabling organizations to efficiently collect, store, process, and analyze large volumes of data. It is a field of expertise within the broader domain of data management and Data Science. Salary of a Data Engineer ranges between ₹ 3.1 Lakhs to ₹ 20.0