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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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Connecting and Reading Data From Azure Data Lake

Analytics Vidhya

Introduction You can access your Azure Data Lake Storage Gen1 directly with the RapidMiner Studio. This is the feature offered by the Azure Data Lake Storage connector. The post Connecting and Reading Data From Azure Data Lake appeared first on Analytics Vidhya.

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Introduction to Azure Data Lake Storage Gen2

Analytics Vidhya

Azure Data Lake Storage is capable of storing large quantities of structured, semi-structured, and unstructured data in […]. The post Introduction to Azure Data Lake Storage Gen2 appeared first on Analytics Vidhya. Introduction ADLS Gen2 The ADLS Gen2 service is built upon Azure Storage as its foundation.

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Top Considerations for Building an Open Cloud Data Lake

Increasingly, enterprises are leveraging cloud data lakes as the platform used to store data for analytics, combined with various compute engines for processing that data. Read this paper to learn about: The value of cloud data lakes as the new system of record.

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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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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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12 Considerations When Evaluating Data Lake Engine Vendors for Analytics and BI

Businesses today compete on their ability to turn big data into essential business insights. To do so, modern enterprises leverage cloud data lakes as the platform used to store data for analytical purposes, combined with various compute engines for processing that data.

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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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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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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.

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Building Best-in-Class Enterprise Analytics

Speaker: Anthony Roach, Director of Product Management at Tableau Software, and Jeremiah Morrow, Partner Solution Marketing Director at Dremio

Register now for the webinar on April 21, 2022 at 10:00 am PDT, 12:00 pm EDT to learn how Dremio and Tableau are delivering mission critical BI and interactive analytics on data directly in the data lake.

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Ultimate Guide to the Cloud Data Lake Engine

Cloud data lake engines aspire to deliver performance and efficiency breakthroughs that make the data lake a viable new home for many mainstream BI workloads. Key takeaways from the guide include: Why you should use a cloud data lake engine. What workloads are suitable for cloud data lake engines.

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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.