AWS Big Data

FEBRUARY 7, 2023

In the data analytics space, organizations often deal with many tables in different databases and file formats to hold data for different business functions. Apache Hudi supports ACID transactions and CRUD operations on a data lake. It uses the native support for Apache Hudi on AWS Glue for Apache Spark.

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Data Lake Testing Big Data Structured Data 75

AWS Big Data

MARCH 25, 2024

To understand the best ways to make API calls via Apache Flink, refer to Common streaming data enrichment patterns in Amazon Kinesis Data Analytics for Apache Flink. OpenSearch Service provides support for native ingestion from Kinesis data streams or MSK topics.

Data Lake 94

Data Lake Unstructured Data Management Modeling 94

Jet Global

OCTOBER 30, 2020

Introducing Data Lakes. Microsoft’s next option is called Azure Data Lake Services (ADLS), and it seems to be the company’s favored long-term solution to its D365 F&SCM reporting challenge. Data lake” is a generic term that refers to a fairly new development in the world of big data analytics.

Data Lake 52

Data Lake Unstructured Data Reporting Finance 52

AWS Big Data

MARCH 27, 2024

Achieving this will also improve general public health through better and more timely interventions, identify health risks through predictive analytics, and accelerate the research and development process. This will be your online transaction processing (OLTP) data store for transactional data.

Data Analytics 95

Data Analytics Analytics Data Warehouse Data Lake 95

IBM Big Data Hub

APRIL 25, 2023

In a prior blog , we pointed out that warehouses, known for high-performance data processing for business intelligence, can quickly become expensive for new data and evolving workloads. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures. Some use case examples will help.

Optimization 56

Optimization Strategy Data Warehouse Cost-Benefit 56

Sisense

APRIL 26, 2020

Structured vs unstructured data. Structured data is far easier for programs to understand, while unstructured data poses a greater challenge. However, both types of data play an important role in data analysis. Structured data. Structured data is organized in tabular format (ie.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

AWS Big Data

NOVEMBER 9, 2023

It allows users to write data transformation code, run it, and test the output, all within the framework it provides. Use case The Enterprise Data Analytics group of a large jewelry retailer embarked on their cloud journey with AWS in 2021. Third-party APIs – These provide analytics and survey data related to ecommerce websites.

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Data Warehouse Testing Data Quality Reporting 109

AWS Big Data

MARCH 27, 2024

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. This facilitates collaboration across isolated clusters, allowing data to be shared for innovation and analytic services.

Testing 102

Testing Data Warehouse Metrics Cost-Benefit 102

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 73

Data Lake Testing Snapshot Sales 73

AWS Big Data

APRIL 20, 2023

Customers use Amazon Redshift to run their business-critical analytics on petabytes of structured and semi-structured data. Apache Spark is a popular framework that you can use to build applications for use cases such as ETL (extract, transform, and load), interactive analytics, and machine learning (ML).

Data Lake 79

Data Lake Data Warehouse Sales Data-driven 79

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. These upstream data sources constitute the data producer components.

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Data Warehouse Data Lake Analytics Data Science 95

AWS Big Data

FEBRUARY 22, 2023

In this post, we show how Ruparupa implemented an incrementally updated data lake to get insights into their business using Amazon Simple Storage Service (Amazon S3), AWS Glue , Apache Hudi , and Amazon QuickSight. An AWS Glue ETL job, using the Apache Hudi connector, updates the S3 data lake hourly with incremental data.

Data Lake 52

Data Lake Dashboards Cost-Benefit Metadata 52

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 122

Unstructured Data Metadata Management Analytics 122

CIO Business Intelligence

MARCH 9, 2022

Applying artificial intelligence (AI) to data analytics for deeper, better insights and automation is a growing enterprise IT priority. 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.

Data Lake 115

Data Lake Unstructured Data Data Warehouse Data Quality 115

AWS Big Data

MAY 28, 2024

The details of each step are as follows: Populate the Amazon Redshift Serverless data warehouse with company stock information stored in Amazon Simple Storage Service (Amazon S3). Redshift Serverless is a fully functional data warehouse holding data tables maintained in real time.

Unstructured Data 56

Unstructured Data Structured Data Data Warehouse Testing 56

IBM Big Data Hub

APRIL 24, 2023

Benefits of new data warehousing technology Everything is data, regardless of whether it’s structured, semi-structured, or unstructured. Most of the enterprise or legacy data warehousing will support only structured data through relational database management system (RDBMS) databases.

Data Warehouse 93

Data Warehouse Data Lake Cost-Benefit Enterprise 93

AWS Big Data

AUGUST 16, 2023

Apache Iceberg is an open table format for very large analytic datasets. 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. We use a sample JSON file as input to Amazon DynamoDB.

Data Lake 73

Data Lake Metadata Testing Snapshot 73

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 71

Data Strategy Strategy Data Warehouse Prescriptive Analytics 71

AWS Big Data

JULY 12, 2023

Introduction to Amazon Redshift Amazon Redshift is a fast, fully-managed, self-learning, self-tuning, petabyte-scale, ANSI-SQL compatible, and secure cloud data warehouse. Thousands of customers use Amazon Redshift to analyze exabytes of data and run complex analytical queries.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52

Corinium

APRIL 25, 2019

Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. Why should Chief Data & Analytics Officers care about data security?

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Insurance Risk IoT Cost-Benefit 150

AWS Big Data

JANUARY 22, 2024

They classified the metrics and indicators in the following categories: Data usage – A clear understanding of who is consuming what data source, materialized with a mapping of consumers and producers. Acast used the Well-Architected Framework for the central account to improve its practice running analytical workloads in the cloud.

Data-driven 89

Data-driven Advertising Metadata Data Architecture 89

Alation

JULY 19, 2022

As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming. Obstacles, such as user roles, permissions, and approval request prevent speedy data access.

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Metadata Data Quality Statistics Data Science 59

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 52

Data Warehouse Data mining Data Lake Dashboards 52

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. These two functions are nearly inseparable as we move further into a world of analytics that blends sources of varying volume, variety, veracity, and velocity. But this was only the tip of the analytics iceberg.

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Modeling Big Data IoT Data Warehouse 84

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. High-value Analytics.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

AWS Big Data

DECEMBER 21, 2023

To speed up the self-service analytics and foster innovation based on data, a solution was needed to provide ways to allow any team to create data products on their own in a decentralized manner. To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse.

Data Lake 93

Data Lake Data Warehouse Data-driven B2B 93

Jet Global

MAY 9, 2024

A data pipeline is a series of processes that move raw data from one or more sources to one or more destinations, often transforming and processing the data along the way. Data pipelines support data science and business intelligence projects by providing data engineers with high-quality, consistent, and easily accessible data.

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Data Lake Data Warehouse Business Intelligence Machine Learning 52