Remove Big Data Remove Data Lakes Remove Data Profiling
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Data Integrity for AI: What’s Old is New Again

Precisely

Then came Big Data and Hadoop! The traditional data warehouse was chugging along nicely for a good two decades until, in the mid to late 2000s, enterprise data hit a brick wall. The big data boom was born, and Hadoop was its poster child. A data lake!

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How data engineers tame Big Data?

Dataconomy

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. They must also ensure that data privacy regulations, such as GDPR and CCPA , are followed.

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Data architecture strategy for data quality

IBM Journey to AI blog

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. LakeFS LakeFS is an open-source platform that provides data lake versioning and management capabilities.

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Comparing Tools For Data Processing Pipelines

The MLOps Blog

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. No built-in data quality functionality. No expert support.