Wayne Yaddow

MARCH 5, 2025

The goal is to examine five major methods of verifying and validating data transformations in data pipelines with an eye toward high-quality data deployment. First, we look at how unit and integration tests uncover transformation errors at an early stage.

Testing 52

Testing Data Transformation Statistics Metadata 52

AWS Big Data

DECEMBER 16, 2024

Third, some services require you to set up and manage compute resources used for federated connectivity, and capabilities like connection testing and data preview arent available in all services. To solve for these challenges, we launched Amazon SageMaker Lakehouse unified data connectivity.

Visualization 79

Visualization Data Processing Testing Publishing 79

Wayne Yaddow

FEBRUARY 26, 2025

AI is transforming how senior data engineers and data scientists validate data transformations and conversions. Artificial intelligence-based verification approaches aid in the detection of anomalies, the enforcement of data integrity, and the optimization of pipelines for improved efficiency.

Data Transformation 52

Data Transformation Testing Data-driven Data Quality 52

Ontotext

AUGUST 24, 2023

The second approach is to use some Data Integration Platform. As an enterprise-supported tool, it has already established how to make all data transformations. Then the recommended approach is to use one of the many JSON to RDF transformation frameworks to produce RDF data. Persistent or non-persistent IDs?

Data Integration 52

Data Integration Modeling Business Objectives Optimization 52

AWS Big Data

AUGUST 19, 2024

As organizations increasingly rely on data stored across various platforms, such as Snowflake , Amazon Simple Storage Service (Amazon S3), and various software as a service (SaaS) applications, the challenge of bringing these disparate data sources together has never been more pressing. Choose Create connection. Choose Next.

Analytics 114

Analytics Data-driven Data Integration Data Lake 114

DataKitchen

DECEMBER 9, 2022

The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes. ChatGPT> DataOps is a term that refers to the set of practices and tools that organizations use to improve the quality and speed of data analytics and machine learning.

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

AWS Big Data

AUGUST 8, 2024

Oracle GoldenGate for Oracle Database and Big Data adapters Oracle GoldenGate is a real-time data integration and replication tool used for disaster recovery, data migrations, high availability. GoldenGate provides special tools called S3 event handlers to integrate with Amazon S3 for data replication.

Analytics 106

Analytics Big Data Software Data Integration 106

AWS Big Data

JANUARY 30, 2025

AWS Glue A data integration service, AWS Glue consolidates major data integration capabilities into a single service. These include data discovery, modern ETL, cleansing, transforming, and centralized cataloging. Its also serverless, which means theres no infrastructure to manage.

Data Warehouse 52

Data Warehouse Data Architecture Machine Learning Data Transformation 52

AWS Big Data

DECEMBER 21, 2023

Movement of data across data lakes, data warehouses, and purpose-built stores is achieved by extract, transform, and load (ETL) processes using data integration services such as AWS Glue. AWS Glue provides both visual and code-based interfaces to make data integration effortless.

Analytics 110

Analytics IT Data Lake Visualization 110

AWS Big Data

OCTOBER 11, 2023

Under the Transparency in Coverage (TCR) rule , hospitals and payors to publish their pricing data in a machine-readable format. For more information, refer to Delivering Consumer-friendly Healthcare Transparency in Coverage On AWS. Then you can use Amazon Athena V3 to query the tables in the Data Catalog.

Visualization 116

Visualization Dashboards Data-driven Gap analysis 116

AWS Big Data

NOVEMBER 13, 2023

As Gameskraft’s portfolio of gaming products increased, it led to an approximate five-times growth of dedicated data analytics and data science teams. Consequently, there was a fivefold rise in data integrations and a fivefold increase in ad hoc queries submitted to the Redshift cluster.

Data Warehouse 109

Data Warehouse Analytics Data Lake Data Science 109

AWS Big Data

JULY 25, 2023

About Talend Talend is an AWS ISV Partner with the Amazon Redshift Ready Product designation and AWS Competencies in both Data and Analytics and Migration. Talend Cloud combines data integration, data integrity, and data governance in a single, unified platform that makes it easy to collect, transform, clean, govern, and share your data.

Data Analytics 52

Data Analytics Analytics Data Warehouse Data Processing 52

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

Alation

JANUARY 17, 2023

What if, experts asked, you could load raw data into a warehouse, and then empower people to transform it for their own unique needs? Today, data integration platforms like Rivery do just that. By pushing the T to the last step in the process, such products have revolutionized how data is understood and analyzed.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Science Data Transformation 52

Occam's Razor

OCTOBER 19, 2010

so you have some reference as to where each item fits (and this will also make it easier for you to pick tools for the priority order referenced in Context #3 above). With those minor caveats, and what it takes to be successful refreshers, I am really excited to tell you all about tools! : ). The Best Web Analytics 2.0

Analytics 136

Analytics Testing Measurement Optimization 136

Alation

OCTOBER 28, 2021

Protect data at the source. Put data into action to optimize the patient experience and adapt to changing business models. What is Data Governance in Healthcare? Data governance in healthcare refers to how data is collected and used by hospitals, pharmaceutical companies, and other healthcare organizations and service providers.

Data Governance 52

Data Governance Measurement Data Quality Metrics 52

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 121

Data Lake Management Metrics Data Warehouse 121

AWS Big Data

MARCH 15, 2023

To populate the database, the Infomedia team developed a data pipeline using Amazon Simple Storage Service (Amazon S3) for data storage, AWS Glue for data transformations, and Apache Hudi for CDC and record-level updates. The following diagram illustrates this architecture.

Cost-Benefit 80

Cost-Benefit Data Processing Optimization Data-driven 80

Cloudera

NOVEMBER 22, 2021

Rise in polyglot data movement because of the explosion in data availability and the increased need for complex data transformations (due to, e.g., different data formats used by different processing frameworks or proprietary applications). As a result, alternative data integration technologies (e.g.,

Data Processing 94

Data Processing Data Warehouse Enterprise Visualization 94

Smarten

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 40

Data Warehouse OLAP Data Governance Optimization 40

AWS Big Data

DECEMBER 2, 2024

More companies have realized there is an opportunity to integrate, enhance, and present this SaaS data to improve internal operations and gain valuable insights on their data. From there, they can perform meaningful analytics, gain valuable insights, and optionally push enriched data back to external SaaS platforms.

Data Lake 97

Data Lake Testing Data Integration Metadata 97

AWS Big Data

JANUARY 29, 2025

To avoid this situation, Oktank aims to decouple compute from storage, allowing them to scale down compute nodes and repurpose them for other workloads without compromising data integrity and accessibility. Additionally, we show you how to submit batch jobs to Amazon EMR using EMR steps for automated, scheduled data processing.

Big Data 76

Big Data Data Analytics Analytics Interactive 76

DataKitchen

JANUARY 23, 2023

It is important to have additional tools and processes in place to understand the impact of data errors and to minimize their effect on the data pipeline and downstream systems. These operations can include data movement, validation, cleaning, transformation, aggregation, analysis, and more.

Testing 130

Testing Data Governance Data Quality Data-driven 130

CIO Business Intelligence

JANUARY 24, 2024

Gather/Insert data on market trends, customer behavior, inventory levels, or operational efficiency. IoT, Web Scraping, API, IDP, RPA Data Processing Data Pipelines and Analysis Layer Employ data pipelines with algorithms to filter, sort, and interpret data, transforming raw information into actionable insights.

IoT 118

IoT Machine Learning Internet of Things Optimization 118

Jet Global

MAY 1, 2023

that gathers data from many sources. Strategic Objective Create a complete, user-friendly view of the data by preparing it for analysis. Requirement Multi-Source Data Blending Data from multiple sources is compiled and the output is a single view, metric, or visualization. Ask your vendors for references.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping is important for several reasons.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Visualization 52

Jet Global

MAY 9, 2024

Data Extraction : The process of gathering data from disparate sources, each of which may have its own schema defining the structure and format of the data and making it available for processing. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52