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Top Data Lakes Interview Questions

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction A data lake is a centralized repository for storing, processing, and securing massive amounts of structured, semi-structured, and unstructured data. It can store data in its native format and process any type of data, regardless of size.

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Key Components and Challenges of Data Lakes

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Today, Data Lake is most commonly used to describe an ecosystem of IT tools and processes (infrastructure as a service, software as a service, etc.) that work together to make processing and storing large volumes of data easy.

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Modernize your legacy databases with AWS data lakes, Part 2: Build a data lake using AWS DMS data on Apache Iceberg

AWS Big Data

This is part two of a three-part series where we show how to build a data lake on AWS using a modern data architecture. This post shows how to load data from a legacy database (SQL Server) into a transactional data lake ( Apache Iceberg ) using AWS Glue. Delete the bucket.

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Databricks Lakehouse Platform Streamlines Big Data Processing

David Menninger's Analyst Perspectives

Databricks is a data engineering and analytics cloud platform built on top of Apache Spark that processes and transforms huge volumes of data and offers data exploration capabilities through machine learning models. The platform supports streaming data, SQL queries, graph processing and machine learning.

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A Detailed Introduction on Data Lakes and Delta Lakes

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction A data lake is a central data repository that allows us to store all of our structured and unstructured data on a large scale. The post A Detailed Introduction on Data Lakes and Delta Lakes appeared first on Analytics Vidhya.

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Incremental refresh for Amazon Redshift materialized views on data lake tables

AWS Big Data

Amazon Redshift is a fast, fully managed cloud data warehouse that makes it cost-effective to analyze your data using standard SQL and business intelligence tools. Customers use data lake tables to achieve cost effective storage and interoperability with other tools.

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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. Data Type and Processing.

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