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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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Diving Deeper into the Data Lake

David Menninger's Analyst Perspectives

A data lake is a centralized repository designed to house big data in structured, semi-structured and unstructured form. I have been covering the data lake topic for several years and encourage you to check out an earlier perspective called Data Lakes: Safe Way to Swim in Big Data?

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Data Lake or Data Warehouse- Which is Better?

Analytics Vidhya

Data collection is critical for businesses to make informed decisions, understand customers’ […]. The post Data Lake or Data Warehouse- Which is Better? We can use it to represent facts, figures, and other information that we can use to make decisions. appeared first on Analytics Vidhya.

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Data Lakes Meet Data Warehouses

David Menninger's Analyst Perspectives

In this analyst perspective, Dave Menninger takes a look at data lakes. He explains the term “data lake,” describes common use cases and shares his views on some of the latest market trends. He explores the relationship between data warehouses and data lakes and share some of Ventana Research’s findings on the subject.

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The Unexpected Cost of Data Copies

Fortunately, a next-gen data architecture enabled by the Dremio data lake service removes the need for replicated data, helping organizations to minimize complexity, boost efficiency and dramatically reduce costs. Read this whitepaper to learn: Why organizations frequently end up with unnecessary data copies.

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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. source_s3_bucket – The raw S3 bucket name. S3FileIO").getOrCreate()

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A Comprehensive Guide to Data Lake vs. Data Warehouse

Analytics Vidhya

Introduction In this constantly growing era, the volume of data is increasing rapidly, and tons of data points are produced every second. Now, businesses are looking for different types of data storage to store and manage their data effectively.

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Checklist Report: Preparing for the Next-Generation Cloud Data Architecture

Data architectures to support reporting, business intelligence, and analytics have evolved dramatically over the past 10 years. Download this TDWI Checklist report to understand: How your organization can make this transition to a modernized data architecture.

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The Next-Generation Cloud Data Lake: An Open, No-Copy Data Architecture

However, they often struggle with increasingly larger data volumes, reverting back to bottlenecking data access to manage large numbers of data engineering requests and rising data warehousing costs. This new open data architecture is built to maximize data access with minimal data movement and no data copies.

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Data Analytics in the Cloud for Developers and Founders

Speaker: Javier Ramírez, Senior AWS Developer Advocate, AWS

Will the data lake scale when you have twice as much data? Is your data secure? In this session, we address common pitfalls of building data lakes and show how AWS can help you manage data and analytics more efficiently. Javier Ramirez will present: The typical steps for building a data lake.