AWS Big Data

JANUARY 6, 2025

Amazon AppFlow automatically encrypts data in motion, and allows you to restrict data from flowing over the public internet for SaaS applications that are integrated with AWS PrivateLink , reducing exposure to security threats. Refer to the Amazon Redshift Database Developer Guide for more details.

Analytics 98

Analytics Data Warehouse Big Data Metrics 98

AWS Big Data

DECEMBER 4, 2024

SageMaker still includes all the existing ML and AI capabilities you’ve come to know and love for data wrangling, human-in-the-loop data labeling with Amazon SageMaker Ground Truth , experiments, MLOps, Amazon SageMaker HyperPod managed distributed training, and more. The tools to transform your business are here.

Data Analytics 102

Data Analytics Analytics Data Lake Data Quality 102

AWS Big Data

SEPTEMBER 4, 2024

Effective data analytics relies on seamlessly integrating data from disparate systems through identifying, gathering, cleansing, and combining relevant data into a unified format. Reverse ETL use cases are also supported, allowing you to write data back to Salesforce.

Data Integration 107

Data Integration Data Lake Data-driven Cost-Benefit 107

AWS Big Data

AUGUST 22, 2024

The infrastructure provides an analytics experience to hundreds of in-house analysts, data scientists, and student-facing frontend specialists. The data engineering team is on a mission to modernize its data integration platform to be agile, adaptive, and straightforward to use.

Data Warehouse 116

Data Warehouse Data Lake Data Integration Management 116

AWS Big Data

DECEMBER 9, 2024

The importance of publishing only high-quality data cant be overstatedits the foundation for accurate analytics, reliable machine learning (ML) models, and sound decision-making. AWS Glue is a serverless data integration service that you can use to effectively monitor and manage data quality through AWS Glue Data Quality.

Data Quality 124

Data Quality Publishing Snapshot Data Lake 124

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 122

Data Lake Cost-Benefit Data-driven Data Warehouse 122

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 145

Data Warehouse Data Lake Visualization Big Data 145

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 119

Data Lake Data Warehouse Metadata Data Architecture 119

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 110

Data Lake Data Warehouse Data-driven Optimization 110

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 118

Data Analytics Analytics Data Warehouse Data Lake 118

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 128

Data Warehouse Analytics Data Lake Machine Learning 128

AWS Big Data

JANUARY 30, 2025

Diagram 1: Overall architecture of the solution, using AWS Step Functions, Amazon Redshift and Amazon S3 The following AWS services were used to shape our new ETL architecture: Amazon Redshift A fully managed, petabyte-scale data warehouse service in the cloud. Its also serverless, which means theres no infrastructure to manage.

Data Warehouse 53

Data Warehouse Data Architecture Machine Learning Data Transformation 53

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 111

Data Warehouse Analytics Data Lake Data Science 111

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 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 92

Snapshot Data Warehouse Data Processing Strategy 92

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 123

Data Governance Unstructured Data Metadata Data Lake 123

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 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

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

datapine

NOVEMBER 11, 2021

This data is usually saved in different databases, external applications, or in an indefinite number of Excel sheets which makes it almost impossible to combine different data sets and update every source promptly. BI tools aim to make data integration a simple task by providing the following features: a) Data Connectors.

Business Intelligence 213

Business Intelligence Dashboards Interactive Visualization 213

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 169

Testing Data Quality Predictive Modeling Metrics 169

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 111

Data Processing Data Lake Data Warehouse Optimization 111

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

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 103

Testing Data Lake Cost-Benefit Data Integration 103

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 125

Metrics Data Lake Cost-Benefit Dashboards 125

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 111

Analytics IT Data Lake Visualization 111

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 113

Data Lake Metadata Data Warehouse Data Processing 113

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 116

Data Warehouse Snapshot Data Processing Internet of Things 116

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 110

Data Lake Big Data Data Warehouse Consulting 110

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 110

Data Architecture Optimization Data Warehouse Metadata 110

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 100

Testing Data Integration Data Lake Enterprise 100

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

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 93

Analytics Data Warehouse Data Lake Data-driven 93

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

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 123

Data Lake Snapshot Metadata Data Architecture 123

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 94

Data Strategy Strategy Data Warehouse Prescriptive Analytics 94

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 93

Modeling Sales Data Warehouse Snapshot 93

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 84

Data Warehouse Data Processing Machine Learning Data Quality 84