AWS Big Data

MARCH 27, 2024

AWS has invested in a zero-ETL (extract, transform, and load) future so that builders can focus more on creating value from data, instead of having to spend time preparing data for analysis. You can send data from your streaming source to this resource for ingesting the data into a Redshift data warehouse.

Data Analytics 99

Data Analytics Analytics Data Warehouse Data Lake 99

AWS Big Data

JUNE 27, 2024

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. This enables you to use your data to acquire new insights for your business and customers. For additional details, refer to Automated snapshots.

Snapshot 75

Snapshot Data Warehouse Data Processing Strategy 75

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 100

Data Lake Data Warehouse Metadata Data Architecture 100

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.

Data Warehouse 98

Data Warehouse Analytics Data Lake Data Science 98

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

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

JULY 25, 2023

The integration of Talend Cloud and Talend Stitch with Amazon Redshift Serverless can help you achieve successful business outcomes without data warehouse infrastructure management. Data scientists, developers, and data analysts can access meaningful insights and build data-driven applications with zero maintenance.

Data Analytics 52

Data Analytics Analytics Data Warehouse Data Processing 52

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

AWS Big Data

FEBRUARY 29, 2024

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. For detailed implementation guidance, refer to Unstructured data management and governance using AWS AI/ML and analytics services.

Data Governance 102

Data Governance Unstructured Data Metadata Data Lake 102

Smart Data Collective

OCTOBER 17, 2022

A point of data entry in a given pipeline. Examples of an origin include storage systems like data lakes, data warehouses and data sources that include IoT devices, transaction processing applications, APIs or social media. The final point to which the data has to be eventually transferred is a destination.

Data Warehouse 132

Data Warehouse Data Lake Visualization Big Data 132

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. These tables are then joined with tables from the Enterprise Data Lake (EDL) at runtime.

Data Processing 99

Data Processing Data Lake Data Warehouse Optimization 99

AWS Big Data

DECEMBER 21, 2023

As the volume and complexity of analytics workloads continue to grow, customers are looking for more efficient and cost-effective ways to ingest and analyse data. OpenSearch Service is used for multiple purposes, such as observability, search analytics, consolidation, cost savings, compliance, and integration.

Analytics 102

Analytics IT Data Lake Visualization 102

Jet Global

JUNE 2, 2024

Since then, simple items that offer multiple solutions to achieve a goal are often referred to as being the Swiss army knife of their kind. You can schedule Atlas reports to refresh automatically, pulling the latest data from Microsoft Dynamics and other data sources. No need for an expensive data warehouse.

Operational Reporting 52

Operational Reporting Data Warehouse Finance Dashboards 52

AWS Big Data

SEPTEMBER 11, 2024

Source systems Aruba’s source repository includes data from three different operating regions in AMER, EMEA, and APJ, along with one worldwide (WW) data pipeline from varied sources like SAP S/4 HANA, Salesforce, Enterprise Data Warehouse (EDW), Enterprise Analytics Platform (EAP) SharePoint, and more.

Data Architecture 89

Data Architecture Optimization Data Warehouse Metadata 89

AWS Big Data

MAY 18, 2023

It’s even harder when your organization is dealing with silos that impede data access across different data stores. Seamless data integration is a key requirement in a modern data architecture to break down data silos. For more details, refer to Spark Release 3.3.0 AWS Glue Data Catalog client 3.6.0

Testing 81

Testing Data Lake Cost-Benefit Data Integration 81

DataKitchen

SEPTEMBER 21, 2023

What is Data in Place? Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. By diligently testing and monitoring data in Use, you uphold data integrity and provide tangible value to end-users.

Testing 173

Testing Data Quality Predictive Modeling Metrics 173

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. This is often a laborious and error-prone process.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52

FineReport

AUGUST 14, 2020

For open-source reporting tools, you can refer to this article? For popular reporting tools on the market, you can refer to: Best Reporting Tools List in 2020 and How to Choose. Based on the process from data to knowledge, a standard reporting system’s functional architecture is shown below.

Reporting 98

Reporting Informatics OLAP Data Warehouse 98

AWS Big Data

DECEMBER 13, 2023

A CDC-based approach captures the data changes and makes them available in data warehouses for further analytics in real-time. usually a data warehouse) needs to reflect those changes in near real-time. This post showcases how to use streaming ingestion to bring data to Amazon Redshift.

Data Warehouse 105

Data Warehouse Snapshot Data Processing Internet of Things 105

erwin

FEBRUARY 7, 2019

For example, manually managing data mappings for the enterprise data warehouse via MS Excel spreadsheets had become cumbersome and unsustainable for one BSFI company. It recognized the need for a solution to standardize the pre-ETL data mapping process to make data integration more efficient and cost-effective.

Metadata 96

Metadata Insurance Data-driven Cost-Benefit 96

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.

Data Lake 90

Data Lake Metadata Data Warehouse Data Processing 90

CIO Business Intelligence

MARCH 22, 2022

One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Another option is a data warehouse, which stores processed and refined data. Set up unified data governance rules and processes.

Analytics 115

Analytics Key Performance Indicator Data Warehouse Data-driven 115

AWS Big Data

NOVEMBER 20, 2023

For any modern data-driven company, having smooth data integration pipelines is crucial. These pipelines pull data from various sources, transform it, and load it into destination systems for analytics and reporting. For instructions, refer to Create a CloudWatch alarm based on a static threshold.

Metrics 102

Metrics Data Lake Cost-Benefit Dashboards 102

AWS Big Data

MARCH 26, 2024

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Data exploration Data exploration helps unearth inconsistencies, outliers, or errors.

Data Strategy 74

Data Strategy Strategy Data Warehouse Prescriptive Analytics 74

AWS Big Data

OCTOBER 20, 2023

Regarding the Azure Data Lake Storage Gen2 Connector, we highlight any major differences in this post. AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning, and application development. For Glue version , choose your AWS Glue version.

Data Lake 90

Data Lake Big Data Consulting Data Warehouse 90

AWS Big Data

JUNE 28, 2023

Another example of AWS’s investment in zero-ETL is providing the ability to query a variety of data sources without having to worry about data movement. Data analysts and data engineers can use familiar SQL commands to join data across several data sources for quick analysis, and store the results in Amazon S3 for subsequent use.

Analytics 69

Analytics Data Warehouse Data Lake Data-driven 69

Ontotext

APRIL 10, 2024

Knowledge graphs and semantic metadata Knowledge graphs (KGs) are the key to: Advanced Data Architecture & Models like Data Fabric, Data Mesh Unified Data Access Semantic Data Integration These fundamental capabilities of KGs enable them to bridge the chasm between information and knowledge in the DIKW pyramid.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

AWS Big Data

FEBRUARY 8, 2023

Vyaire developed a custom data integration platform, iDataHub, powered by AWS services such as AWS Glue , AWS Lambda , and Amazon API Gateway. In this post, we share how we extracted data from SAP ERP using AWS Glue and the SAP SDK. For more information, refer to Download and Installation of NW RFC SDK.

Testing 79

Testing Data Integration Data Lake Enterprise 79

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. Data ingestion/integration services. Reverse ETL tools.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

AWS Big Data

JULY 19, 2023

Amazon Redshift is a fully managed and petabyte-scale cloud data warehouse that is used by tens of thousands of customers to process exabytes of data every day to power their analytics workload. You can structure your data, measure business processes, and get valuable insights quickly can be done by using a dimensional model.

Modeling 69

Modeling Sales Data Warehouse Snapshot 69

Ontotext

AUGUST 22, 2023

Worse, and according to Gartner, upward of 80% of enterprise data today is unstructured which further exacerbates the loss of knowledge, insights, and the wisdom needed to make effective business choices. As a result, organizations are looking for fresh data integration approaches to challenge the mindset with which we created them.

Metadata 52

Metadata Knowledge Discovery Big Data Enterprise 52

BI-Survey

FEBRUARY 17, 2020

From operational systems to support “smart processes”, to the data warehouse for enterprise management, to exploring new use cases through advanced analytics : all of these environments incorporate disparate systems, each containing data fragments optimized for their own specific task. .

Data Governance 89

Data Governance Data Warehouse Data Quality Data Strategy 89

Jet Global

OCTOBER 7, 2020

Confusing matters further, Microsoft has also created something called the Data Entity Store, which serves a different purpose and functions independently of data entities. The Data Entity Store is an internal data warehouse that is only available to embedded Power BI reports (not the full version of Power BI).

Data Warehouse 90

Data Warehouse OLAP Reporting Finance 90

Cloudera

APRIL 14, 2021

Refer to the following cloudera blog to understand the full potential of Cloudera Data Engineering. . Precisely Data Integration, Change Data Capture and Data Quality tools support CDP Public Cloud as well as CDP Private Cloud. For further details on the API, please refer to the following doc link here. .

Data Warehouse 82

Data Warehouse Data Processing Data Quality Machine Learning 82

AWS Big Data

APRIL 3, 2024

Manage your Iceberg table with AWS Glue You can use AWS Glue to ingest, catalog, transform, and manage the data on Amazon Simple Storage Service (Amazon S3). With AWS Glue, you can discover and connect to more than 70 diverse data sources and manage your data in a centralized data catalog. Nidhi Gupta is a Sr.

Data Lake 100

Data Lake Snapshot Metadata Data Architecture 100

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

AWS Big Data

JULY 31, 2023

Customers often use many SQL scripts to select and transform the data in relational databases hosted either in an on-premises environment or on AWS and use custom workflows to manage their ETL. AWS Glue is a serverless data integration and ETL service with the ability to scale on demand. Select s3_crawler and choose Run.

Sales 52

Sales Data Warehouse Visualization Testing 52