Smart Data Collective

AUGUST 28, 2021

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.

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Smart Data Collective

SEPTEMBER 23, 2020

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. Data Warehouse.

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Data Virtualization

APRIL 21, 2022

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? In a way, the name describes what.

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Analytics Vidhya

FEBRUARY 25, 2023

Introduction A data lake is a centralized and scalable repository storing structured and unstructured data. The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

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TDAN

SEPTEMBER 14, 2021

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.” That makes sense. I think the […].

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Analytics Vidhya

JULY 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction We are all pretty much familiar with the common modern cloud data warehouse model, which essentially provides a platform comprising a data lake (based on a cloud storage account such as Azure Data Lake Storage Gen2) AND a data warehouse compute engine […].

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AWS Big Data

APRIL 3, 2024

licensed, 100% open-source data table format that helps simplify data processing on large datasets stored in data lakes. Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time.

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IBM Big Data Hub

NOVEMBER 15, 2018

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.

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Data Virtualization

OCTOBER 18, 2022

The post The Data Warehouse is Dead, Long Live the Data Warehouse, Part I appeared first on Data Virtualization blog - Data Integration and Modern Data Management Articles, Analysis and Information. In times of potentially troublesome change, the apparent paradox and inner poetry of these.

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ScienceSoft

FEBRUARY 28, 2020

Learn about the difference between a data lake and a big data warehouse, and define how to structure your big data solution in accordance with your business needs.

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Smart Data Collective

MARCH 30, 2022

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. Determine your preparedness.

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AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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DataCamp

JANUARY 6, 2020

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

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IBM Big Data Hub

FEBRUARY 2, 2023

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery.

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TDAN

JANUARY 3, 2023

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. Then, at a later point in time, the end user analyst can come along and […].

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KDnuggets

JANUARY 16, 2020

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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Jet Global

NOVEMBER 5, 2020

OLAP reporting has traditionally relied on a data warehouse. Again, this entails creating a copy of the transactional data in the ERP system, but it also involves some preprocessing of data into so-called “cubes” so that you can retrieve aggregate totals and present them much faster. Option 3: Azure Data Lakes.

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David Menninger's Analyst Perspectives

SEPTEMBER 16, 2021

It has a drag and drop visual interface and can connect to databases, enterprise data warehouses, data lakes, cloud storage, business applications and social media. The platform also supports push-down processing for data prep and ETL inside databases to minimize data movement and optimize performance.

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Jet Global

SEPTEMBER 4, 2020

For more sophisticated multidimensional reporting functions, however, a more advanced approach to staging data is required. The Data Warehouse Approach. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible.

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AWS Big Data

MARCH 2, 2023

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. and later supports the Apache Iceberg framework for data lakes. AWS Glue 3.0 The following diagram illustrates the solution architecture.

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AWS Big Data

JUNE 23, 2023

Events and many other security data types are stored in Imperva’s Threat Research Multi-Region data lake. Imperva harnesses data to improve their business outcomes. As part of their solution, they are using Amazon QuickSight to unlock insights from their data.

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AWS Big Data

MARCH 27, 2023

Amazon Redshift Serverless makes it simple to run and scale analytics without having to manage your data warehouse infrastructure. Analytics Specialist based out of Northern Virginia, specialized in the design and implementation of analytics and data lake solutions.

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Data Virtualization

NOVEMBER 24, 2022

Reading Time: 4 minutes My previous post explained that, in my mind, the data lakehouse differs hardly at all from the traditional data warehouse architectural design pattern (ADP). It consists largely of the application of new cloud-based technology to the same requirements and constraints.

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AWS Big Data

OCTOBER 17, 2023

With Amazon Redshift, you can use standard SQL to query data across your data warehouse, operational data stores, and data lake. Migrating a data warehouse can be complex. You have to migrate terabytes or petabytes of data from your legacy system while not disrupting your production workload.

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Sisense

NOVEMBER 12, 2020

Data warehouse vs. databases Traditional vs. Cloud Explained Cloud data warehouses in your data stack A data-driven future powered by the cloud. We live in a world of data: There’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Data warehouse vs. databases.

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Analytics Vidhya

JANUARY 2, 2023

Introduction Delta Lake is an open-source storage layer that brings data lakes to the world of Apache Spark. Delta Lakes provides an ACID transaction–compliant and cloud–native platform on top of cloud object stores such as Amazon S3, Microsoft Azure Storage, and Google Cloud Storage.

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AWS Big Data

FEBRUARY 24, 2023

Data lakes are a popular choice for today’s organizations to store their data around their business activities. As a best practice of a data lake design, data should be immutable once stored. A data lake built on AWS uses Amazon Simple Storage Service (Amazon S3) as its primary storage environment.

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Data Virtualization

MARCH 16, 2023

Reading Time: 6 minutes Data lake, by combining the flexibility of object storage with the scalability and agility of cloud platforms, are becoming an increasingly popular choice as an enterprise data repository. Whether you are on Amazon Web Services (AWS) and leverage AWS S3.

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Data Virtualization

MARCH 16, 2023

Reading Time: 6 minutes Data lake, by combining the flexibility of object storage with the scalability and agility of cloud platforms, are becoming an increasingly popular choice as an enterprise data repository. Whether you are on Amazon Web Services (AWS) and leverage AWS S3.

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O'Reilly on Data

FEBRUARY 19, 2021

The Right Solution for Your Data: Cloud Data Lakes and Data Lakehouses. Data lakes have experienced a fairly robust resurgence over the last few years, specifically cloud data lakes. A Wave of Cloud-Native, Distributed Data Frameworks.

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Cloudera

JANUARY 15, 2021

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. The results demonstrate superior price performance of Cloudera Data Warehouse on the full set of 99 queries from the TPC-DS benchmark. Introduction.

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Jet Global

JULY 2, 2019

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.

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Sisense

DECEMBER 23, 2019

AI and ML are the only ways to derive value from massive data lakes, cloud-native data warehouses, and other huge stores of information. The right data and analytics platform can help you bridge the gap between your current AI and analytics paradigm and where you want your company to be in the future. .

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erwin

AUGUST 15, 2022

For NoSQL, data lakes, and data lake houses—data modeling of both structured and unstructured data is somewhat novel and thorny. This blog is an introduction to some advanced NoSQL and data lake database design techniques (while avoiding common pitfalls) is noteworthy. Data modeling basics.

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