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.

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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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 […].

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.

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.

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.

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

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.

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.

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.

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.

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.

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.”

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.

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?

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

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

Teradata

Martin Wilcox examines the failure of data lakes

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.

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.

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.

Data Virtualization: The Key to a Successful Data Lake

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 lake projects will actually fail.

Data Virtualization: The Key to a Successful Data Lake

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 lake projects will actually fail.

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.

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.

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?

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.

Build a cost-efficient data lake strategy with The Denodo Platform

Data Virtualization

The market for data lakes has recently seen an impressive wave of new-generation engines that provide highly efficient processing of very large data volumes stored in distributed file systems, like S3, ADLS and others.

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.

Build a cost-efficient data lake strategy with The Denodo Platform

Data Virtualization

The market for data lakes has recently seen an impressive wave of new-generation engines that provide highly efficient processing of very large data volumes stored in distributed file systems, like S3, ADLS and others.

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.

Data Lakes vs. Data Warehouses

DataCamp

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

Race Ahead of Threats with a Security Data Lake

Sirius Computer Solutions

The proliferation of data only complicates matters. The complexity and cost of SIEM solutions and the number of resources that security consumes can easily swallow a large portion of an enterprise’s budget, causing many organizations to fall behind in the security data race.

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.

Unlocking the Potential of Machine Learning in a Data Lake

Data Virtualization

With data becoming the brain food to the intelligence of every organization, regardless of size or sector, it has become crucial to harness this data to achieve the best results, make the most informed decisions and improve productivity. Technology artificial intelligence big data Data integration Data Lake data virtualization Logical Data Lake Machine learning

Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

Consultants and developers familiar with the AX data model could query the database using any number of different tools, including a myriad of different report writers. Data Entities. Currently, over 1,700 data entities are available and counting. The Data Warehouse Approach.

7 Key Benefits of Proper Data Lake Ingestion

Smart Data Collective

It’s impossible to deny the importance of data in several industries, but that data can get overwhelming if it isn’t properly managed. The reality is businesses that are collecting data will likely be doing so on several levels. Covering Data Types.

Data Lakes on Cloud & it’s Usage in Healthcare

BizAcuity

Data lakes are centralized repositories that can store all structured and unstructured data at any desired scale. Data can be stored as-is, without first structuring it, and different types of analytics can be run on it, from dashboards and visualizations to big data processing, real-time analytics, and machine learning to improve decision making. The power of the data lake lies in the fact that it often is a cost-effective way to store data.

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.

How to Implement Data Engineering in Practice?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Image Source: GitHub Table of Contents What is Data Engineering? The post How to Implement Data Engineering in Practice?

What's the difference between data lakes and data warehouses?

IBM Big Data Hub

If you’ve heard the debate among IT professionals about data lakes versus data warehouses, you might be wondering which is better for your organization. You might even be wondering how these two approaches are different at all

Complexity Drives Costs: A Look Inside BYOD and Azure Data Lakes

Jet Global

The Data Security Problem: How We Got Here. You can extract data from relational databases, including Microsoft’s SQL Server using the SQL query language. In addition to reading data, however, you can also use the SQL language to insert, update, or delete records from a database.

Rapidminer Platform Supports Entire Data Science Lifecycle

David Menninger's Analyst Perspectives

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics.

Data Lakes: What Are They and Who Needs Them?

Jet Global

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. From the humble database through to data warehouses , data stores have grown both in scale and complexity to keep pace with the businesses they serve, and the data analysis now required to remain competitive. What’s in a Data Lake? Data warehouses do a great job of standardizing data from disparate sources for analysis.