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 88

Enterprise Data Warehouse Snapshot Cost-Benefit 88

Peter James Thomas

AUGUST 18, 2018

Convergent Evolution refers to something else. That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. One of the early promises of a Data Lake approach was that – once all relevant data had been ingested – this would be directly leveraged by Data Scientists to derive insight.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

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 79

Metadata Data Lake Data Processing Data-driven 79

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? Do they have a system to manage the metadata for given assets? Make sure that the value of data increases over time.

Data Governance 52

Data Governance Strategy Data Quality Marketing 52

AWS Big Data

FEBRUARY 1, 2023

With data volumes exhibiting a double-digit percentage growth rate year on year and the COVID pandemic disrupting global logistics in 2021, it became more critical to scale and generate near-real-time data. You can visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

IBM Big Data Hub

JANUARY 17, 2023

A data lakehouse is an emerging data management architecture that improves efficiency and converges data warehouse and data lake capabilities driven by a need to improve efficiency and obtain critical insights faster. Let’s start with why data lakehouses are becoming increasingly important.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Peter James Thomas

AUGUST 29, 2018

I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently Data Lakes and Analytics , constantly building experience and capability in the Data Governance , Quality and data services domains, both inside banks, as a consultant and as a vendor.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

AWS Big Data

DECEMBER 18, 2023

Solution overview One of the common functionalities involved in data pipelines is extracting data from multiple data sources and exporting it to a data lake or synchronizing the data to another database. For more information, refer to IAM Policies for invoking AWS Glue job from Step Functions.

Metadata 121

Metadata Visualization Data Lake Data-driven 121

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. Then, you transform this data into a concise format.

Data Strategy 72

Data Strategy Strategy Data Warehouse Prescriptive Analytics 72

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. Historic Balance – compares current data to previous or expected values.

Testing 157

Testing Metadata Dashboards Statistics 157

Alation

JULY 19, 2022

A data catalog can assist directly with every step, but model development. And even then, information from the data catalog can be transferred to a model connector , allowing data scientists to benefit from curated metadata within those platforms. How Data Catalogs Help Data Scientists Ask Better Questions.

Metadata 59

Metadata Data Quality Statistics Data Science 59

AWS Big Data

JUNE 12, 2023

This schema serves as a single source of truth for producer and consumer and you can leverage the schema evolution feature of AWS Glue Schema Registry to keep it consistent as the data changes over time. Refer appendix section for more information on this feature. Refer to the first stack’s output.

Management 80

Management Metadata Testing Internet of Things 80

AWS Big Data

NOVEMBER 15, 2023

As the organization receives data from multiple external vendors, it often arrives in different formats, typically Excel or CSV files, with each vendor using their own unique data layout and structure. DataBrew is an excellent tool for data quality and preprocessing. For Matching conditions , choose Match all conditions.

Metadata 88

Metadata Sales Data Lake Big Data 88

Ontotext

FEBRUARY 26, 2024

Firstly, on the data maturity spectrum, the vast majority of organizations I’ve spoken with are stuck in the information stage. They have massive amounts of data they’re collecting and storing in their relational databases, document stores, data lakes, and data warehouses. RDF vs LPG?

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

Alation

JUNE 21, 2022

Analysts didn’t just want to catalog data sources, they wanted to include dashboards, reports, and visualizations. 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 engineers want to catalog data pipelines.

Metadata 52

Metadata Data Quality Visualization Data Lake 52

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. To learn more about DataZone, refer to the User Guide. Bienvenue dans DataZone!

Data Lake 95

Data Lake Metadata Data Governance Statistics 95

erwin

AUGUST 31, 2023

It delivers the ability to capture and unify the business and technical perspectives of data assets, enables effective collaboration between a variety of stakeholders, and delivers metadata-driven automation to accelerate the creation and maintenance of data sources on virtually any data management platform.

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

IBM Big Data Hub

AUGUST 4, 2023

In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 65

Data Architecture Data Lake Machine Learning Data Governance 65

erwin

JULY 17, 2019

Your organization won’t be able to take complete advantage of analytics tools to become data-driven unless you establish a foundation for agile and complete data management. You need automated data mapping and cataloging through the integration lifecycle process, inclusive of data at rest and data in motion.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

Andrew White

JANUARY 11, 2021

It was titled, The Gartner 2021 Leadership Vision for Data & Analytics Leaders. This was for the Chief Data Officer, or head of data and analytics. It is meant to be a desk-reference for that role for 2021. Does Data warehouse as a software tool will play role in future of Data & Analytics strategy?

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

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 Sales 52