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 97

Data Warehouse Data Lake Analytics Machine Learning 97

AWS Big Data

FEBRUARY 1, 2023

This introduces the need for both polling and pushing the data to access and analyze in near-real time. From an operational standpoint, we designed a new shared responsibility model for data ingestion using AWS Glue instead of internal services (REST APIs) designed on Amazon EC2 to extract the data.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

CIO Business Intelligence

OCTOBER 13, 2023

Data architect Armando Vázquez identifies eight common types of data architects: Enterprise data architect: These data architects oversee an organization’s overall data architecture, defining data architecture strategy and designing and implementing architectures.

Data Architecture 131

Data Architecture Data Warehouse Statistics Visualization 131

AWS Big Data

APRIL 29, 2024

Example permission configuration In a practical application within a company, permissions for tables and fields in the data warehouse are divided based on business departments, isolating sensitive data for different business units. This provides data security and orderly conduct of daily business operations.

Management 89

Management Big Data Metadata Data Warehouse 89

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 81

Enterprise Data Warehouse Data Lake Optimization 81

Cloudera

DECEMBER 10, 2020

Why worry about costs with cloud-native data warehousing? Have you been burned by the unexpected costs of a cloud data warehouse? If not, before adopting a cloud data warehouse, consider the true costs of a cloud-native data warehouse. These costs impede the adoption of cloud-native data warehouses.

Data Warehouse 98

Data Warehouse Metadata Cost-Benefit Optimization 98

Sisense

SEPTEMBER 3, 2019

This blog is intended to give an overview of the considerations you’ll want to make as you build your Redshift data warehouse to ensure you are getting the optimal performance. Amazon describes the dense storage nodes (DS2) as optimized for large data workloads and use hard disk drives (HDD) for storage.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78

AWS Big Data

DECEMBER 21, 2023

To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse. In this post, we show how smava optimized their data platform by using Amazon Redshift Serverless and Amazon Redshift data sharing to overcome right-sizing challenges for unpredictable workloads and further improve price-performance.

Data Lake 85

Data Lake Data Warehouse B2B Data-driven 85

AWS Big Data

NOVEMBER 15, 2023

BMW Group uses 4,500 AWS Cloud accounts across the entire organization but is faced with the challenge of reducing unnecessary costs, optimizing spend, and having a central place to monitor costs. The ultimate goal is to raise awareness of cloud efficiency and optimize cloud utilization in a cost-effective and sustainable manner.

Dashboards 91

Dashboards Analytics Metadata Optimization 91

AWS Big Data

AUGUST 31, 2023

Amazon Redshift is a fast, fully managed petabyte-scale cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Amazon Redshift also supports querying nested data with complex data types such as struct, array, and map.

Data Lake 89

Data Lake Data Warehouse Metadata Data Architecture 89

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 80

Enterprise Data Warehouse Snapshot Cost-Benefit 80

erwin

JUNE 26, 2020

Given the value this sort of data-driven insight can provide, the reason organizations need a data catalog should become clearer. It’s no surprise that most organizations’ data is often fragmented and siloed across numerous sources (e.g., Three Types of Metadata in a Data Catalog. Technical Metadata.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

AWS Big Data

NOVEMBER 27, 2023

Amazon Redshift , the most widely used cloud data warehouse, has evolved significantly to meet the performance requirements of the most demanding workloads. This post covers one such new feature—the multidimensional data layout sort key. Refer to Working with automatic table optimization for more details on ATO.

Cost-Benefit 96

Cost-Benefit Data Warehouse Optimization Testing 96

Cloudera

AUGUST 31, 2021

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

Data Warehouse 80

Data Warehouse Cost-Benefit Metadata Data-driven 80

Ontotext

MAY 2, 2024

Storage-centric approach In the storage-centric approach, people try to address data silos by throwing everything in a data lake or a data warehouse. But, although, this helps somewhat in terms of architecture, soon these data lakes become unwieldy. The best way to drive value is through use cases.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

IBM Big Data Hub

APRIL 28, 2022

A data fabric can simplify data access in an organization to facilitate self-service data consumption, while remaining agnostic to data environments, processes, utility and geography. Obtaining access to each data warehouse and subsequently drawing relationships between the data would be a cumbersome process.

Metadata 83

Metadata Data Warehouse Forecasting Predictive Modeling 83

AWS Big Data

JANUARY 24, 2024

These formats enable ACID (atomicity, consistency, isolation, durability) transactions, upserts, and deletes, and advanced features such as time travel and snapshots that were previously only available in data warehouses. The output will give a count of the number of data and metadata files deleted.

Snapshot 96

Snapshot Data Lake Metadata Optimization 96

AWS Big Data

APRIL 19, 2023

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. We split the solution into two primary components: generating Spark job metadata and running the SQL on Amazon EMR. The script generates a metadata JSON file for each step.

Metadata 76

Metadata Testing Data Lake Consulting 76

AWS Big Data

OCTOBER 4, 2023

An Amazon DataZone domain contains an associated business data catalog for search and discovery, a set of metadata definitions to decorate the data assets that are used for discovery purposes, and data projects with integrated analytics and ML tools for users and groups to consume and publish data assets.

Metadata 96

Metadata Data Lake Publishing Data Governance 96

AWS Big Data

MARCH 26, 2024

Profile aggregation – When you’ve uniquely identified a customer, you can build applications in Managed Service for Apache Flink to consolidate all their metadata, from name to interaction history. Then, you transform this data into a concise format. Let’s find out what role each of these components play in the context of C360.

Data Strategy 67

Data Strategy Strategy Data Warehouse Prescriptive Analytics 67

AWS Big Data

NOVEMBER 22, 2023

Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance. For CoW tables, queries see the latest data committed.

Data Lake 89

Data Lake Snapshot Metadata Optimization 89

CIO Business Intelligence

JUNE 23, 2022

These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Data Lake 90

Data Lake Data Warehouse Machine Learning Cost-Benefit 90

Cloudera

JUNE 18, 2022

These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Data Lake 89

Data Lake Data Warehouse Machine Learning Cost-Benefit 89

Cloudera

JANUARY 19, 2024

It can help you to create, edit, optimize, fix, and succinctly summarize queries using natural language. This is a real game-changer for data analysts on all levels and will make SQL development faster, easier, and less error-prone. The optimize and the fix functionality do not need user input.

Data Warehouse 71

Data Warehouse Data Processing Optimization Modeling 71

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 109

Data Lake Metadata Optimization Statistics 109

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 fetch the metadata of the users_xxxxxx table from Athena.

Data Lake 68

Data Lake Metadata Testing Snapshot 68

AWS Big Data

JANUARY 24, 2023

This solution only replicates metadata in the Data Catalog, not the actual underlying data. To have a redundant data lake using Lake Formation and AWS Glue in an additional Region, we recommend replicating the Amazon S3-based storage using S3 replication , S3 sync, aws-s3-copy-sync-using-batch or S3 Batch replication process.

Data Architecture 64

Data Architecture Metadata Data Lake Snapshot 64

Cloudera

JULY 14, 2023

While these instructions are carried out for Cloudera Data Platform (CDP), Cloudera Data Engineering, and Cloudera Data Warehouse, one can extrapolate them easily to other services and other use cases as well. You could optimize your table now or at a later stage using the “rewrite_data_files” procedure.

Snapshot 69

Snapshot Metadata Data Warehouse Testing 69

AWS Big Data

MARCH 2, 2023

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. 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.

Data Lake 114

Data Lake Data Processing Metadata Snapshot 114

AWS Big Data

MARCH 7, 2023

Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. They should also provide optimal performance with low or no tuning. Data lakes are more focused around storing and maintaining all the data in an organization in one place.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

AWS Big Data

FEBRUARY 21, 2023

Data warehouses play a vital role in healthcare decision-making and serve as a repository of historical data. A healthcare data warehouse can be a single source of truth for clinical quality control systems. What is a dimensional data model? What is a dimensional data model? What is a data vault?

Data Warehouse 63

Data Warehouse Data Lake Cost-Benefit Modeling 63

AWS Big Data

NOVEMBER 29, 2023

You also need services to store data for analysis and machine learning (ML) like Amazon Simple Storage Service (Amazon S3). Customers have created hundreds of thousands of data lakes on Amazon S3. It does all of this while factoring in your price/performance targets so you can optimize between cost and performance.

Data Warehouse 109

Data Warehouse Data Lake Data Integration Dashboards 109

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

Risk 75

Risk Modeling Management Metadata 75

Cloudera

NOVEMBER 12, 2020

Burst to Cloud not only relieves pressure on your data center, but it also protects your VIP applications and users by giving them optimal performance without breaking your bank. Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs. You are probably hesitant.

Data Warehouse 101

Data Warehouse Reporting Risk Cost-Benefit 101

AWS Big Data

SEPTEMBER 25, 2023

With Amazon Redshift , you can run a complex mix of workloads on your data warehouse, such as frequent data loads running alongside business-critical dashboard queries and complex transformation jobs. We also see more and more data science and machine learning (ML) workloads. ExampleCorp has multiple Redshift clusters.

Management 91

Management Data Warehouse Metrics Dashboards 91

Jet Global

FEBRUARY 23, 2024

Data sources are crucial for reporting, analyzing, and acting on transactional and corporate data and connecting these sources in real time with various tools like connectors, ETL tools, mashups, Web services, and data source-neutral BI solutions is essential. Data warehouses can be complex, time-consuming, and expensive.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

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. When you want to access the data back, you can bulk restore the archived objects. Jupyter Enterprise Gateway 2.6.0,

Data Lake 80

Data Lake Snapshot Metadata Optimization 80