AWS Big Data

SEPTEMBER 25, 2023

With Amazon Redshift , you can run a complex mix of workloads on your data warehouse, such as frequent data loads running alongside business-critical dashboard queries and complex transformation jobs. We also see more and more data science and machine learning (ML) workloads. ExampleCorp has multiple Redshift clusters.

Management 94

Management Data Warehouse Metrics Dashboards 94

erwin

JULY 2, 2020

a senior business process management architect at a pharma/biotech company with more than 5,000 employees, erwin Evolve was useful for enterprise architecture reference. As he put it, “We are describing our business process and we are trying to describe our data catalog. Data Modeling with erwin Data Modeler. George H.,

Enterprise 122

Enterprise Modeling Metadata Data Governance 122

AWS Big Data

JANUARY 31, 2023

Data governance is the collection of policies, processes, and systems that organizations use to ensure the quality and appropriate handling of their data throughout its lifecycle for the purpose of generating business value.

Data Lake 79

Data Lake Data Governance Data Architecture Data Warehouse 79

Alation

FEBRUARY 13, 2020

Data curation is important in today’s world of data sharing and self-service analytics, but I think it is a frequently misused term. When speaking and consulting, I often hear people refer to data in their data lakes and data warehouses as curated data, believing that it is curated because it is stored as shareable data.

Metadata 52

Metadata Data Warehouse Data Lake Data Governance 52

Alation

FEBRUARY 20, 2020

As data drives more and more of the modern economy, data governance and data management are racing to keep up with an ever-expanding range of requirements, constraints and opportunities. Prior to the Big Data revolution, companies were inward-looking in terms of data. THE NEED FOR METADATA TOOLS.

Metadata 52

Metadata IT Data Governance Data Warehouse 52

datapine

SEPTEMBER 29, 2022

Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. But first, let’s define what data quality actually is. What is the definition of data quality? Why Do You Need Data Quality Management? 2 – Data profiling.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Alation

DECEMBER 21, 2022

Transactional systems and data warehouses can then use the golden records as the entity’s most current, trusted representation. Data Catalog and Master Data Management. Early on, analysts used data catalogs to find and understand data more quickly. Downstream consumers. MDM Build Objects.

Management 52

Management Data Quality Metadata Testing 52

Cloudera

SEPTEMBER 15, 2022

Mark: The first element in the process is the link between the source data and the entry point into the data platform. At Ramsey International (RI), we refer to that layer in the architecture as the foundation, but others call it a staging area, raw zone, or even a source data lake. What is a data fabric?

Data Lake 79

Data Lake Data Architecture Data-driven Data Warehouse 79

Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

Data Quality 98

Data Quality Data Governance Metadata Metrics 98

AWS Big Data

MAY 7, 2024

Data governance is a key enabler for teams adopting a data-driven culture and operational model to drive innovation with data. Amazon DataZone allows you to simply and securely govern end-to-end data assets stored in your Amazon Redshift data warehouses or data lakes cataloged with the AWS Glue data catalog.

Metadata 74

Metadata Data Lake Data Processing Data-driven 74

Domino Data Lab

APRIL 3, 2019

Paco Nathan ‘s latest column dives into data governance. This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Alation

APRIL 6, 2023

Data governance is traditionally applied to structured data assets that are most often found in databases and information systems. This blog focuses on governing spreadsheets that contain data, information, and metadata, and must themselves be governed. Simply put, metadata adds context.

Data Governance 52

Data Governance Metadata Cost-Benefit Structured Data 52

erwin

FEBRUARY 7, 2019

Organization’s cannot hope to make the most out of a data-driven strategy, without at least some degree of metadata-driven automation. The volume and variety of data has snowballed, and so has its velocity. As such, traditional – and mostly manual – processes associated with data management and data governance have broken down.

Metadata 96

Metadata Insurance Data-driven Cost-Benefit 96

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

AWS Big Data

JULY 28, 2023

Organizations must comply with these requests provided that there are no legitimate grounds for retaining the personal data, such as legal obligations or contractual requirements. Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Tags provide metadata about resources at a glance.

Snapshot 80

Snapshot Metadata Measurement Data Warehouse 80

AWS Big Data

JANUARY 24, 2023

AWS Lake Formation helps with enterprise data governance and is important for a data mesh architecture. It works with the AWS Glue Data Catalog to enforce data access and governance. This solution only replicates metadata in the Data Catalog, not the actual underlying data.

Data Architecture 64

Data Architecture Metadata Data Lake Snapshot 64

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. A typical modern data stack consists of the following: A data warehouse.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. Then, you transform this data into a concise format. Let’s find out what role each of these components play in the context of C360.

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

AWS Big Data

JULY 21, 2023

This leads to having data across many instances of data warehouses and data lakes using a modern data architecture in separate AWS accounts. We recently announced the integration of Amazon Redshift data sharing with AWS Lake Formation. See Managing LF-Tags for metadata access control for more details.

Data Lake 84

Data Lake Data Warehouse Marketing Management 84

Alation

JANUARY 25, 2022

As a reminder, here’s Gartner’s definition of data fabric: “A design concept that serves as an integrated layer (fabric) of data and connecting processes. In this blog, we will focus on the “integrated layer” part of this definition by examining each of the key layers of a comprehensive data fabric in more detail.

Metadata 52

Metadata IT Metrics Data-driven 52

erwin

SEPTEMBER 25, 2020

With each stage of data modeling, the data model becomes more information- and context-rich. A conceptual data model is a rough draft, containing the relevant concepts or entities and the relationships between them. A logical data model, also referred to as information modeling, is the second stage of data modeling.

Modeling 111

Modeling Cost-Benefit Visualization Metadata 111

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Less data gets decompressed, deserialized, loaded into memory, run through the processing, etc.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Alation

MARCH 1, 2022

Lineage helps them identify the source of bad data to fix the problem fast. Manual lineage will give ARC a fuller picture of how data was created between AWS S3 data lake, Snowflake cloud data warehouse and Tableau (and how it can be fixed). Time is money,” said Leonard Kwok, Senior Data Analyst, ARC.

Data Warehouse 52

Data Warehouse Data Lake Risk Data-driven 52

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale. Frequent table maintenance needs to be performed to prevent read performance from degrading over time.

Data Lake 113

Data Lake Metadata Optimization Statistics 113

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 64

Data Architecture Data Lake Machine Learning Data Governance 64

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

Alation

JUNE 30, 2022

We are now seeing a similar transformation in the world of data, where there’s tension between the old world (single-source-of-truth data warehouses with top-down data governance) and the new world (distributed, self-service analytics with grassroots management). Data quality can change with time.

Metadata 52

Metadata Data Warehouse Data Quality Data-driven 52

Laminar Security

MAY 3, 2023

Six Important Takeaways Around DSPM #1: Organizations are Rapidly Adopting DSPM Solutions to Combat Shadow Data “By 2026, more than 20% of organizations will deploy DSPM technology, due to the urgent requirements to identify and locate previously unknown data repositories and to mitigate associated security and privacy risks.”

Management 52

Management Risk Risk Management Data Processing 52

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. See recorded webinars: Emerging Practices for a Data-driven Strategy. Link Data to Business Outcomes.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Jet Global

FEBRUARY 23, 2024

ETL is beneficial for larger data volumes and diverse sources, and may be necessary for data architects, developers, and administrators considering factors like volume, source diversity, accuracy, and efficiency. Data Migration Data migration refers to the process of transferring data from one location or format to another.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52