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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. Data Lakes are an important […].

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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.)

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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. Our data lake research has uncovered some points to consider in your efforts, and I’d like to offer a deeper dive into our findings.

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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.

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Top Considerations for Building an Open Cloud Data Lake

In this paper, we explore the top considerations for building a cloud data lake including architectural principles, when to use cloud data lake engines and how to empower non-technical users.

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How Data Lake in IoT Analytics Works?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction There are different ways and platforms for managing and organizing big data. It provides a complete and authoritative data repository supporting data analytics, business intelligence, and machine learning.

Data Lake 216
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Data Lake or Data Warehouse- Which is Better?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Data is defined as information that has been organized in a meaningful way. Data collection is critical for businesses to make informed decisions, understand customers’ […].

Data Lake 230
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Data Warehouses, Data Marts and Data Lakes

Analytics Vidhya

Introduction All data mining repositories have a similar purpose: to onboard data for reporting intents, analysis purposes, and delivering insights. By their definition, the types of data it stores and how it can be accessible to users differ.

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Why Your Data Lake Needs Bad Data

David Menninger's Analyst Perspectives

Everyone talks about data quality, as they should. Our research shows that improving the quality of information is the top benefit of data preparation activities. Data quality efforts are focused on clean data. Yes, clean data is important. but so is bad data.

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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.

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Data Warehouse vs Data Lake: Differences Explained

DataFloq

We experience the great impact of data both on our lives and business. But those great amounts of data must be stored and analyzed in an effective way. It is a crucial part of an organization as the data stored is a valuable asset. Big Data

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Ultimate Guide to the Cloud Data Lake Engine

This guide describes how to evaluate cloud data lake engine offerings based on their ability to deliver on their promise of improving performance, data accessibility, and operational efficiency as compared with earlier methods of querying the data lake.

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Architecture for the Data Lake

The Data Administration Newsletter

For a while now, vendors have been advocating that people put their data in a data lake when they put their data in the cloud. The Data Lake The idea is that you put your data into a data lake.

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Builiding a Data Lake using Snowflake

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction to Snowflake When we talk about the “doing” part of data engineering, people usually want to say something about ETL, like, “How is your ETL working today?”

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An Overview of Using Azure Data Lake Storage Gen2

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Before seeing the practical implementation of the use case, let’s briefly introduce Azure Data Lake Storage Gen2 and the Paramiko module.

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Introduction to Azure Data Lake Storage Gen2

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Azure Data Lake Storage is capable of storing large quantities of structured, semi-structured, and unstructured data in […].

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What are the differences between Data Lake and Data Warehouse?

Analytics Vidhya

Overview Understand the meaning of data lake and data warehouse We will see what are the key differences between Data Warehouse and Data Lake. The post What are the differences between Data Lake and Data Warehouse?

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

A next-gen cloud data lake architecture has emerged that brings together the best attributes of the data warehouse and the data lake. This new open data architecture is built to maximize data access with minimal data movement and no data copies.

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A Guide to Build your Data Lake in AWS

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction Data Lake architecture for different use cases – Elegant. The post A Guide to Build your Data Lake in AWS appeared first on Analytics Vidhya.

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Data Lakes and SQL: A Match Made in Data Heaven

KDnuggets

In this article, we will discuss the benefits of using SQL with a data lake and how it can help organizations unlock the full potential of their data. KDnuggets Originals Data Engineering

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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. The stored data is unprocessed, and the structure is usually applied when it is retrieved.

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The Definitive Guide to Data Warehouse vs. Data Lake vs. Data Lakehouse

DataFloq

Struggling to harness data sprawl, CIOs across industries are facing tough challenges. One of them is where to store all of their enterprise’s data to deliver robust data analytics. There have traditionally been two storage solutions for data: data warehouses and data lakes.

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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. Modern enterprises leverage cloud data lakes as the platform used to store data. 57% of the enterprises currently using a data lake cite improved business agility as a benefit.

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Differences Between Data Lake and Data Warehouses

The Data Administration Newsletter

Data lake is a newer IT term created for a new category of data store. But just what is a data lake? According to IBM, “a data lake is a storage repository that holds an enormous amount of raw or refined data in native format until it is accessed.”

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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

The market for data warehouses is booming. While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes.

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Important Considerations When Migrating to a Data Lake

Smart Data Collective

Azure Data Lake Storage Gen2 is based on Azure Blob storage and offers a suite of big data analytics features. If you don’t understand the concept, you might want to check out our previous article on the difference between data lakes and data warehouses.

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Is Data Virtualization the Secret Behind Operationalizing Data Lakes?

Data Virtualization

Reading Time: 4 minutes The amount of expanding volume and variety of data originating from various sources are a massive challenge for businesses. In attempts to overcome their big data challenges, organizations are exploring data lakes as repositories where huge volumes and varieties of.

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The Unexpected Cost of Data Copies

This paper will discuss why organizations frequently end up with multiple data copies and how a secure "no-copy" data strategy enabled by the Dremio data lake service can help reduce complexity, boost efficiency, and dramatically reduce costs.

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The Data Lake is Dead; Long Live the Data Lake!

Teradata

Martin Wilcox examines the failure of data lakes

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Business Case for leveraging Machine Learning (ML) to Validate Data Lake

DataFloq

Without effective and comprehensive validation, a data lake becomes a data swamp and does not offer a clear link to value creation to business. Organizations are rapidly adopting Cloud Data Lake as the data lake of choice. Big Data big data quality

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Schema Evolution in Data Lakes

KDnuggets

Whereas a data warehouse will need rigid data modeling and definitions, a data lake can store different types and shapes of data. In a data lake, the schema of the data can be inferred when it’s read, providing the aforementioned flexibility.

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The Data Lakehouse: Blending Data Warehouses and Data Lakes

Data Virtualization

Reading Time: 3 minutes First we had data warehouses, then came data lakes, and now the new kid on the block is the data lakehouse. But what is a data lakehouse and why should we develop one?

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Subsurface: The Ultimate Data Lakehouse Conference

Speaker: Panel Speakers

We’ve just opened registration for Subsurface LIVE 2023! Learn how to innovate with open source technologies such as Apache Arrow, Delta Lake, and more. Register now to secure your spot at Subsurface LIVE being held March 1-2, 2023.

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Delta Lake: A Comprehensive Introduction

Analytics Vidhya

Introduction Delta Lake is an open-source storage layer that brings data lakes to the world of Apache Spark. It enables organizations to quickly and reliably build data lakes on cloud […].

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Deriving Value from Data Lakes with AI

Sisense

Artificial Intelligence and machine learning are the future of every industry, especially data and analytics. Data is growing at a phenomenal rate and that’s not going to stop anytime soon. Once your data is prepared for analysis, the next question is: how else can AI help you?

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Data Swamp, Data Lake, Data Lakehouse: What to Know

Alation

Data Swamp vs Data Lake. When you imagine a lake, it’s likely an idyllic image of a tree-ringed body of reflective water amid singing birds and dabbling ducks. I’ll take the lake, thank you very much. And so will your data. Benefits of a Data Lake.

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The Differences Between Data Warehouses and Data Lakes

Sisense

The amount of data being generated and stored every day has exploded. Companies of all kinds are sitting on stockpiles of data that could someday prove valuable. Instead, businesses are increasingly turning to data lakes to store massive amounts of unstructured data.

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

Data architectures have evolved dramatically. It is time to reconsider the fundamental ways that information is accumulated, managed, and then provisioned to the different downstream data consumers.

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Modern Data Architecture: Data Warehousing, Data Lakes, and Data Mesh Explained

Data Virtualization

Reading Time: 3 minutes At the heart of every organization lies a data architecture, determining how data is accessed, organized, and used. For this reason, organizations must periodically revisit their data architectures, to ensure that they are aligned with current business goals.

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Here’s Why Automation For Data Lakes Could Be Important

Smart Data Collective

Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings. Analytics Magazine notes that data lakes are among the most useful tools that an enterprise may have at its disposal when aiming to compete with competitors via innovation. There were a lot of promises made about Big Data that fell at the feet of data scientists to make happen. Big Data is, well…big.

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Data Lakes vs. Data Warehouses

DataCamp

Understand the differences between the two most popular options for storing big data

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Data Virtualization: The Key to a Successful Data Lakes

Data Virtualization

If you’ve decided to implement a data lake, you might want to keep Gartner’s assessment in mind, which is that about 80% of all data lakes projects will actually fail.

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Data Analytics in the Cloud for Developers and Founders

Speaker: Javier Ramírez, Senior AWS Developer Advocate, AWS

You have lots of data, and you are probably thinking of using the cloud to analyze it. But how will you move data into the cloud? In which format? How will you validate and prepare the data? What about streaming data? Can data scientists discover and use the data? Can business people create reports via drag and drop? Can operations monitor what’s going on? 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.