AWS Big Data

JUNE 10, 2024

One of the key challenges in modern big data management is facilitating efficient data sharing and access control across multiple EMR clusters. Organizations have multiple Hive data warehouses across EMR clusters, where the metadata gets generated. Test access using SageMaker Studio in the consumer account.

Data Lake 90

Data Lake Metadata Data Warehouse Data Processing 90

AWS Big Data

JUNE 21, 2023

These types of queries are suited for a data warehouse. The goal of a data warehouse is to enable businesses to analyze their data fast; this is important because it means they are able to gain valuable insights in a timely manner. Amazon Redshift is fully managed, scalable, cloud data warehouse.

Data Warehouse 84

Data Warehouse Data Lake OLAP Cost-Benefit 84

AWS Big Data

SEPTEMBER 13, 2023

A modern data architecture is an evolutionary architecture pattern designed to integrate a data lake, data warehouse, and purpose-built stores with a unified governance model. Of those tables, some are larger (such as in terms of record volume) than others, and some are updated more frequently than others.

Data Lake 97

Data Lake Data Processing Metadata Snapshot 97

AWS Big Data

AUGUST 22, 2024

In this post, we discuss how the Kaplan data engineering team implemented data integration from the Salesforce application to Amazon Redshift. Solution overview The high-level data flow starts with the source data stored in Amazon S3 and then integrated into Amazon Redshift using various AWS services.

Data Warehouse 104

Data Warehouse Data Lake Data Integration Management 104

AWS Big Data

JULY 21, 2023

This leads to having data across many instances of data warehouses and data lakes using a modern data architecture in separate AWS accounts. We recently announced the integration of Amazon Redshift data sharing with AWS Lake Formation.

Data Lake 87

Data Lake Data Warehouse Marketing Management 87

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. For more details on tagging, refer to Tagging resources overview. For more tagging best practices, refer to Tagging AWS resources. Choose Save changes. About the Authors Sandeep Bajwa is a Sr.

Data Warehouse 85

Data Warehouse Management Snapshot Data Lake 85

Jet Global

NOVEMBER 5, 2020

That stands for “bring your own database,” and it refers to a model in which core ERP data are replicated to a separate standalone database used exclusively for reporting. OLAP reporting has traditionally relied on a data warehouse. Option 3: Azure Data Lakes. Data lakes are not a mature technology.

Data Lake 98

Data Lake OLAP Data Warehouse Unstructured Data 98

Jet Global

SEPTEMBER 4, 2020

Its solution was to replicate data from the production database, using data entities, into a traditional relational database. Microsoft referred to this approach as “bring your own database” (BYOD). For more sophisticated multidimensional reporting functions, however, a more advanced approach to staging data is required.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

Data Lake 119

Data Lake Metadata Statistics Optimization 119

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.

Data Lake 84

Data Lake Metadata Testing Data Warehouse 84

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.

Data Lake 120

Data Lake Data Processing Metadata Snapshot 120

AWS Big Data

MAY 24, 2023

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. For more information, refer to Retry Amazon S3 requests with EMRFS. availability.

Data Lake 85

Data Lake Snapshot Metadata Optimization 85

AWS Big Data

OCTOBER 9, 2024

Today, customers are embarking on data modernization programs by migrating on-premises data warehouses and data lakes to the AWS Cloud to take advantage of the scale and advanced analytical capabilities of the cloud. The following diagram illustrates this use case’s historical data migration architecture.

Data Quality 89

Data Quality Data Lake Data Warehouse Metrics 89

AWS Big Data

MAY 20, 2024

Whether you are new to Apache Iceberg on AWS or already running production workloads on AWS, this comprehensive technical guide offers detailed guidance on foundational concepts to advanced optimizations to build your transactional data lake with Apache Iceberg on AWS. He can be reached via LinkedIn. He can be reached via LinkedIn.

Data Lake 103

Data Lake Cost-Benefit Big Data Data Warehouse 103

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.

Data Warehouse 91

Data Warehouse Data Processing Data Lake Management 91

AWS Big Data

APRIL 27, 2023

Amazon Athena supports the MERGE command on Apache Iceberg tables, which allows you to perform inserts, updates, and deletes in your data lake at scale using familiar SQL statements that are compliant with ACID (Atomic, Consistent, Isolated, Durable).

Data Lake 74

Data Lake Snapshot Optimization Data Transformation 74

BI-Survey

JANUARY 15, 2023

Statements from countless interviews with our customers reveal that the data warehouse is seen as a “black box” by many and understood by few business users. Therefore, it is not clear why the costly and apparently flexibility-inhibiting data warehouse is needed at all. The limiting factor is rather the data landscape.

Data Warehouse 52

Data Warehouse IT Data Architecture Measurement 52

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.

Data Lake 52

Data Lake Modeling Unstructured Data Data Warehouse 52

AWS Big Data

MARCH 28, 2023

In a data warehouse, a dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. 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 74

Data Lake Testing Snapshot Sales 74

AWS Big Data

JUNE 26, 2024

New feature: Custom AWS service blueprints Previously, Amazon DataZone provided default blueprints that created AWS resources required for data lake, data warehouse, and machine learning use cases. If you’re new to Amazon DataZone, refer to Getting started.

Data Lake 96

Data Lake Data Warehouse Unstructured Data Data Governance 96

AWS Big Data

JUNE 25, 2024

This post is co-authored by Vijay Gopalakrishnan, Director of Product, Salesforce Data Cloud. In today’s data-driven business landscape, organizations collect a wealth of data across various touch points and unify it in a central data warehouse or a data lake to deliver business insights.

Data Lake 105

Data Lake Cost-Benefit Data-driven Data Warehouse 105

DataKitchen

AUGUST 19, 2021

Figure 3 shows an example processing architecture with data flowing in from internal and external sources. Each data source is updated on its own schedule, for example, daily, weekly or monthly. The data scientists and analysts have what they need to build analytics for the user. The new Recipes run, and BOOM! Conclusion.

Data Warehouse 246

Data Warehouse Data Lake Manufacturing Testing 246

AWS Big Data

AUGUST 21, 2024

The current scaling approach of Amazon Redshift Serverless increases your compute capacity based on the query queue time and scales down when the queuing reduces on the data warehouse. This post also includes example SQLs, which you can run on your own Redshift Serverless data warehouse to experience the benefits of this feature.

Optimization 82

Optimization Data Lake Data Warehouse Cost-Benefit 82

AWS Big Data

FEBRUARY 16, 2023

Amazon Redshift is the most widely used data warehouse in the cloud, best suited for analyzing exabytes of data and running complex analytical queries. Amazon QuickSight is a fast business analytics service to build visualizations, perform ad hoc analysis, and quickly get business insights from your data.

Data Warehouse 81

Data Warehouse Sales Visualization Data Processing 81

CIO Business Intelligence

APRIL 25, 2024

Currently, a handful of startups offer “reverse” extract, transform, and load (ETL), in which they copy data from a customer’s data warehouse or data platform back into systems of engagement where business users do their work. Sharing Customer 360 insights back without data replication.

Data Integration 103

Data Integration Data Lake Data Warehouse Metadata 103

AWS Big Data

MAY 3, 2023

In today’s data-driven business environment, organizations face the challenge of efficiently preparing and transforming large amounts of data for analytics and data science purposes. Businesses need to build data warehouses and data lakes based on operational data.

Data Lake 87

Data Lake Data Warehouse Data-driven Optimization 87

AWS Big Data

FEBRUARY 16, 2024

Many customers are extending their data warehouse capabilities to their data lake with Amazon Redshift. They are looking to further enhance their security posture where they can enforce access policies on their data lakes based on Amazon Simple Storage Service (Amazon S3).

Data Lake 105

Data Lake Data Warehouse Testing Business Objectives 105

AWS Big Data

NOVEMBER 29, 2023

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

Data Warehouse 106

Data Warehouse Data Lake Analytics Machine Learning 106

AWS Big Data

NOVEMBER 7, 2024

BladeBridge offers a comprehensive suite of tools that automate much of the complex conversion work, allowing organizations to quickly and reliably transition their data analytics capabilities to the scalable Amazon Redshift data warehouse. times better price performance than other cloud data warehouses.

Data Warehouse 77

Data Warehouse Reporting Big Data Data Lake 77

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 90

Enterprise Data Warehouse Data Lake Optimization 90

AWS Big Data

AUGUST 5, 2024

These business units have varying landscapes, where a data lake is managed by Amazon Simple Storage Service (Amazon S3) and analytics workloads are run on Amazon Redshift , 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.

Data Lake 83

Data Lake Finance Sales Management 83

AWS Big Data

MARCH 7, 2024

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 118

Data Lake Analytics Dashboards Metrics 118

AWS Big Data

MARCH 27, 2024

These services enable you to collect and analyze data in near real time and put a comprehensive data governance framework in place that uses granular access control to secure sensitive data from unauthorized users. This will be your online transaction processing (OLTP) data store for transactional data.

Data Analytics 98

Data Analytics Analytics Data Warehouse Data Lake 98

AWS Big Data

NOVEMBER 10, 2023

This integration expands the possibilities for AWS analytics and machine learning (ML) solutions, making the data warehouse accessible to a broader range of applications. Your applications can seamlessly read from and write to your Amazon Redshift data warehouse while maintaining optimal performance and transactional consistency.

Data Processing 99

Data Processing Data Lake Data Warehouse Optimization 99

AWS Big Data

FEBRUARY 29, 2024

First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

Data Governance 102

Data Governance Unstructured Data Metadata Data Lake 102

AWS Big Data

SEPTEMBER 4, 2024

With the AWS Glue Salesforce connector, you can ingest and transform your CRM data to any of the AWS Glue supported destinations, including Amazon Simple Storage Service (Amazon S3), in your preferred format, including Apache Iceberg, Apache Hudi, and Linux Foundation Delta Lake; data warehouses such as Amazon Redshift and Snowflake; and many more.

Data Integration 83

Data Integration Data Lake Data-driven Cost-Benefit 83

CIO Business Intelligence

JUNE 29, 2022

Azure Data Factory. Data Explorer is a big data analytics platform that you can use, as the name suggests, for exploring data using KQL, also known as the Kusto Query Language, from the codename for the project which may or may not be a reference to exploring your ocean of data as if you were Jacques Cousteau.

Data Lake 109

Data Lake Analytics Data Warehouse Machine Learning 109