Big Data, Data Lake, Data Science and Structured Data

Big Data

Data Lake

Data Science

Structured Data

Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

SEPTEMBER 23, 2020

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. Both data warehouses and data lakes are used when storing big data.

Data Lake

Data Lake Data Warehouse Unstructured Data Big Data

Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science

Data Science Data Analytics Prescriptive Analytics Analytics

Join 52,000+

Insiders

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Webinars

Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

MORE WEBINARS

Trending Sources

How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

MARCH 7, 2023

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

Analytics

Analytics Data Warehouse Data Lake Metadata

Webinars

Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

MORE WEBINARS

How SumUp made digital analytics more accessible using AWS Glue

AWS Big Data

JUNE 6, 2023

Unless, of course, the rest of their data also resides in the Google Cloud. In this post we showcase how we used AWS Glue to move siloed digital analytics data, with inconsistent arrival times, to AWS S3 (our Data Lake) and our central data warehouse (DWH), Snowflake. It consists of full-day and intraday tables.

Analytics

Analytics Data Lake Testing Optimization

Implement slowly changing dimensions in a data lake using AWS Glue and Delta

AWS Big Data

MARCH 28, 2023

As organizations across the globe are modernizing their data platforms with data lakes on Amazon Simple Storage Service (Amazon S3), handling SCDs in data lakes can be challenging.

Data Lake

Data Lake Testing Snapshot Sales

How GamesKraft uses Amazon Redshift data sharing to support growing analytics workloads

AWS Big Data

NOVEMBER 13, 2023

Amazon Redshift is a fully managed data warehousing service that offers both provisioned and serverless options, making it more efficient to run and scale analytics without having to manage your data warehouse. Additionally, data is extracted from vendor APIs that includes data related to product, marketing, and customer experience.

Data Warehouse

Data Warehouse Data Lake Analytics Data Science

Building a Beautiful Data Lakehouse

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake

Data Lake Unstructured Data Data Warehouse Data Quality

In-depth with CDO Christopher Bannocks

Peter James Thomas

AUGUST 29, 2018

I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently Data Lakes and Analytics , constantly building experience and capability in the Data Governance , Quality and data services domains, both inside banks, as a consultant and as a vendor.

Data-driven

Data-driven Cost-Benefit Metadata Technology

Big Data Fabric Weaves Together Automation, Scalability, and Intelligence

Cloudera

JANUARY 22, 2019

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge. What is Big Data Fabric?

Big Data

Big Data Data Lake Internet of Things Enterprise

Unstructured data management and governance using AWS AI/ML and analytics services

AWS Big Data

OCTOBER 25, 2023

Most companies produce and consume unstructured data such as documents, emails, web pages, engagement center phone calls, and social media. By some estimates, unstructured data can make up to 80–90% of all new enterprise data and is growing many times faster than structured data.

Unstructured Data

Unstructured Data Metadata Management Analytics

Implement a serverless CDC process with Apache Iceberg using Amazon DynamoDB and Amazon Athena

AWS Big Data

AUGUST 16, 2023

Iceberg manages large collections of files as tables, and it supports modern analytical data lake operations such as record-level insert, update, delete, and time travel queries. Iceberg also helps guarantee data correctness under concurrent write scenarios. We use a sample JSON file as input to Amazon DynamoDB.

Data Lake

Data Lake Metadata Testing Snapshot

Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

MARCH 26, 2024

The AWS modern data architecture shows a way to build a purpose-built, secure, and scalable data platform in the cloud. Learn from this to build querying capabilities across your data lake and the data warehouse. About the Authors Ismail Makhlouf is a Senior Specialist Solutions Architect for Data Analytics at AWS.

Data Strategy

Data Strategy Strategy Data Warehouse Prescriptive Analytics

Create a Value Blizzard with Snowflake and Microsoft Azure

CDW Research Hub

DECEMBER 4, 2019

Cloud-based data warehouses can also perform complex analytical queries much faster due to the use of massively parallel processing (MPP), which uses multiple processors—each with its own operating system and memory—to simultaneously perform a set of coordinated computations.

Data Warehouse

Data Warehouse Data mining Data Lake Dashboards

What is a Data Pipeline?

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake

Data Lake Data Warehouse Business Intelligence Machine Learning

Data Leaders Brief

Differentiating Between Data Lakes and Data Warehouses

Data science vs data analytics: Unpacking the differences

Webinars

Trending Sources

How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

Webinars

How SumUp made digital analytics more accessible using AWS Glue

Implement slowly changing dimensions in a data lake using AWS Glue and Delta

How GamesKraft uses Amazon Redshift data sharing to support growing analytics workloads

Building a Beautiful Data Lakehouse

In-depth with CDO Christopher Bannocks

Big Data Fabric Weaves Together Automation, Scalability, and Intelligence

Unstructured data management and governance using AWS AI/ML and analytics services

Implement a serverless CDC process with Apache Iceberg using Amazon DynamoDB and Amazon Athena

Create an end-to-end data strategy for Customer 360 on AWS

Create a Value Blizzard with Snowflake and Microsoft Azure

What is a Data Pipeline?

Stay Connected