AWS Big Data

DECEMBER 4, 2024

Domain ownership recognizes that the teams generating the data have the deepest understanding of it and are therefore best suited to manage, govern, and share it effectively. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

Metadata 94

Metadata Data Governance Data Quality Data-driven 94

AWS Big Data

JUNE 6, 2023

You can use AWS Glue to create, run, and monitor data integration and ETL (extract, transform, and load) pipelines and catalog your assets across multiple data stores. Hundreds of thousands of customers use data lakes for analytics and ML to make data-driven business decisions.

Data Quality 83

Data Quality Data-driven Data Lake Metrics 83

AWS Big Data

JUNE 6, 2023

Poor-quality data can lead to incorrect insights, bad decisions, and lost opportunities. AWS Glue Data Quality measures and monitors the quality of your dataset. It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines.

Data Quality 76

Data Quality Data Lake Visualization Data-driven 76

AWS Big Data

DECEMBER 4, 2024

These formats, exemplified by Apache Iceberg, Apache Hudi, and Delta Lake, addresses persistent challenges in traditional data lake structures by offering an advanced combination of flexibility, performance, and governance capabilities. For more information, refer to What are deletion vectors?

Snapshot 84

Snapshot Metadata Data Lake Optimization 84

AWS Big Data

JULY 25, 2024

Solution To address the challenge, ATPCO sought inspiration from a modern data mesh architecture. In Amazon DataZone, data owners can publish their data and its business catalog (metadata) to ATPCO’s DataZone domain. Data consumers can then search for relevant data assets using these human-friendly metadata terms.

Data Lake 106

Data Lake Metadata Sales Publishing 106

AWS Big Data

MAY 1, 2024

For those reasons, it was extremely difficult for Fujitsu to manage and utilize data at scale with Excel. Solution overview OneData defines three personas: Publisher – This role includes the organizational and management team of systems that serve as data sources. It is crucial in data governance and data management.

Dashboards 122

Dashboards Publishing Data-driven Cost-Benefit 122

DataKitchen

OCTOBER 11, 2021

Figure 2: Example data pipeline with DataOps automation. In this project, I automated data extraction from SFTP, the public websites, and the email attachments. The automated orchestration published the data to an AWS S3 Data Lake. All the code, Talend job, and the BI report are version controlled using Git.

Testing 152

Testing Metadata Dashboards Statistics 152

AWS Big Data

JANUARY 18, 2024

AWS Lake Formation and the AWS Glue Data Catalog form an integral part of a data governance solution for data lakes built on Amazon Simple Storage Service (Amazon S3) with multiple AWS analytics services integrating with them. In 2022 , we talked about the enhancements we had done to these services. Bien intégré!

Data Lake 105

Data Lake Metadata Data Governance Statistics 105

AWS Big Data

DECEMBER 19, 2024

Data has become an invaluable asset for businesses, offering critical insights to drive strategic decision-making and operational optimization. Delta tables technical metadata is stored in the Data Catalog, which is a native source for creating assets in the Amazon DataZone business catalog.

Data Governance 52

Data Governance Publishing Data-driven Metadata 52

AWS Big Data

MAY 7, 2024

It also makes it easier for engineers, data scientists, product managers, analysts, and business users to access data throughout an organization to discover, use, and collaborate to derive data-driven insights. Note that a managed data asset is an asset for which Amazon DataZone can manage permissions.

Metadata 87

Metadata Data Lake Data Processing Data-driven 87

AWS Big Data

JANUARY 31, 2023

Data governance is increasingly top-of-mind for customers as they recognize data as one of their most important assets. Effective data governance enables better decision-making by improving data quality, reducing data management costs, and ensuring secure access to data for stakeholders.

Data Lake 90

Data Lake Data Governance Data Architecture Machine Learning 90

datapine

APRIL 15, 2020

You will need to continually return to your business dashboard to make sure that it’s working, the data is accurate and it’s still answering the right questions in the most effective way. Testing will eliminate lots of data quality challenges and bring a test-first approach through your agile cycle.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

IBM Big Data Hub

APRIL 27, 2022

Since its uniquely metadata-driven, the abstraction layer of a data fabric makes it easier to model, integrate and query any data sources, build data pipelines, and integrate data in real-time. This improves data engineering productivity and time-to-value for data consumers. What’s a data mesh?

Management 79

Management Metadata Data Architecture Data Lake 79

AWS Big Data

AUGUST 1, 2024

Migrating to Amazon Redshift offers organizations the potential for improved price-performance, enhanced data processing, faster query response times, and better integration with technologies such as machine learning (ML) and artificial intelligence (AI). When the wave is complete, the people from that wave will move to another wave.

Data Warehouse 100

Data Warehouse KPI Optimization Cost-Benefit 100

AWS Big Data

MARCH 7, 2023

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

Analytics 79

Analytics Data Warehouse Data Lake Metadata 79

AWS Big Data

FEBRUARY 13, 2025

This plane drives users to engage in data-driven conversations with knowledge and insights shared across the organization. Through the product experience plane, data product owners can use automated workflows to capture data lineage and data quality metrics and oversee access controls.

Data Analytics 72

Data Analytics Analytics Modeling Management 72

AWS Big Data

APRIL 4, 2024

Griffin is an open source data quality solution for big data, which supports both batch and streaming mode. In today’s data-driven landscape, where organizations deal with petabytes of data, the need for automated data validation frameworks has become increasingly critical.

Data Quality 71

Data Quality Data Lake Data Warehouse Data-driven 71

AWS Big Data

DECEMBER 9, 2024

Given the importance of data in the world today, organizations face the dual challenges of managing large-scale, continuously incoming data while vetting its quality and reliability. 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 109

Data Quality Publishing Snapshot Data Lake 109

Cloudera

SEPTEMBER 9, 2022

This is the promise of the modern data lakehouse architecture. analyst Sumit Pal, in “Exploring Lakehouse Architecture and Use Cases,” published January 11, 2022: “Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support AI, BI, ML, and data engineering on a single platform.”

Metadata 102

Metadata Machine Learning Unstructured Data Data Lake 102

Ontotext

FEBRUARY 12, 2024

Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage. Establish and enforce data governance by ensuring all data used is accurate, complete, and compliant with regulations. For instance, JPMorgan Chase & Co.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

erwin

AUGUST 31, 2023

Improved Decision Making : Well-modeled data provides insights that drive informed decision-making across various business domains, resulting in enhanced strategic planning. Reduced Data Redundancy : By eliminating data duplication, it optimizes storage and enhances data quality, reducing errors and discrepancies.

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

Ontotext

FEBRUARY 12, 2024

It proposes a technological, architectural, and organizational approach to solving data management problems by breaking up the monolithic data platform and de-centralizing data management across different domain teams and services. Some examples of data products are data sets, tables, machine learning models, and APIs.

IT 64

IT Metadata Data Quality Data Lake 64

Alation

JUNE 21, 2022

Why start with a data source and build a visualization, if you can just find a visualization that already exists, complete with metadata about it? Data scientists went beyond database tables to data lakes and cloud data stores. Data scientists want to catalog not just information sources, but models.

Metadata 52

Metadata Data Quality Visualization Data Lake 52

AWS Big Data

MARCH 3, 2023

It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more often and 106 times faster, recover from incidents 2,604 times faster, and release 7 times fewer defects. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

AWS Big Data

JULY 25, 2024

With AWS Glue, you can discover and connect to hundreds of different data sources and manage your data in a centralized data catalog. You can visually create, run, and monitor ETL pipelines to load data into your data lakes.

Visualization 105

Visualization Management Data Integration Testing 105

Ontotext

NOVEMBER 16, 2023

Data mesh solves this by promoting data autonomy, allowing users to make decisions about domains without a centralized gatekeeper. It also improves development velocity with better data governance and access with improved data quality aligned with business needs.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. This produces end-to-end lineage so business and technology users alike can understand the state of a data lake and/or lake house.

Metadata 52

Metadata Cost-Benefit Data Quality Data Lake 52

AWS Big Data

JUNE 12, 2023

Therefore, it’s crucial to keep the schema definition in the Schema Registry and the Data Catalog table in sync. To avoid this, it’s recommended to use a data quality check mechanism to identify such anomalies and take appropriate action in case of unexpected behavior. page in the GitHub repository. $

Management 87

Management Metadata Internet of Things Testing 87

Peter James Thomas

AUGUST 12, 2018

These normally appear at the end of an article, but it seemed to make sense to start with them in this case: Recently I published Building Momentum – How to begin becoming a Data-driven Organisation. These and other areas are covered in greater detail in an older article, Using BI to drive improvements in data quality.

Metrics 49

Metrics Statistics Data Quality Measurement 49

Alation

JANUARY 26, 2023

Offer the right tools Data stewardship is greatly simplified when the right tools are on hand. So ask yourself, does your steward have the software to spot issues with data quality, for example? 2) Always Remember Compliance Source: Unsplash There are now many different data privacy and security laws worldwide.

Data Governance 52

Data Governance Strategy Data Quality Marketing 52

Alation

NOVEMBER 28, 2022

It’s impossible for data teams to assure the data quality of such spreadsheets and govern them all effectively. If unaddressed, this chaos can lead to data quality, compliance, and security issues. In an enterprise, there may be thousands of spreadsheets used for critical business decisions.

Metadata 52

Metadata Enterprise Cost-Benefit Finance 52

Andrew White

JANUARY 11, 2021

This was for the Chief Data Officer, or head of data and analytics. Gartner also published the same piece of research for other roles, such as Application and Software Engineering. Does Data warehouse as a software tool will play role in future of Data & Analytics strategy? We have published some case studies.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Jet Global

JANUARY 30, 2025

Its distributed architecture empowers organizations to query massive datasets across databases, data lakes, and cloud platforms with speed and reliability. Optimizing connections to your data sources is equally important, as it directly impacts the speed and efficiency of data access.

Data Lake 64

Data Lake Data-driven Optimization Enterprise 64

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. 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

datapine

AUGUST 29, 2022

Due to this book being published recently, there are not any written reviews available. 4) Big Data: Principles and Best Practices Of Scalable Real-Time Data Systems by Nathan Marz and James Warren. 6) Lean Analytics: Use Data to Build a Better Startup Faster, by Alistair Croll and Benjamin Yoskovitz.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Jet Global

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Visualization 52

BI-Survey

FEBRUARY 27, 2025

Advanced: Does it leverage AI/ML to enrich metadata by automatically linking glossary entries with data assets and performing semantic tagging? Leading-edge: Does it provide data quality or anomaly detection features to enrich metadata with quality metrics and insights, proactively identifying potential issues?

Unstructured Data 52

Unstructured Data Metadata Data Quality Data Governance 52