Cloudera

MARCH 4, 2024

In this article, we will walk you through the process of implementing fine grained access control for the data governance framework within the Cloudera platform. In a good data governance strategy, it is important to define roles that allow the business to limit the level of access that users can have to their strategic data assets.

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Precisely

SEPTEMBER 8, 2023

According to a recent report on data integrity trends from Drexel University’s LeBow College of Business , 41% reported that data governance was a top priority for their data programs. Automating functions in support of data governance provides a range of important benefits.

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Precisely

SEPTEMBER 5, 2023

As this realization grows, businesses are shifting their investments from hardware to technologies that optimize data assets. Master Data Management systems (MDM) play an important role in harmonizing data assets across large and midsize enterprises.

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Precisely

SEPTEMBER 6, 2023

.” Poor data quality impedes the success of data programs, hampers data integration efforts, limits data integrity causing big data governance challenges. To truly succeed in an increasingly data-driven world, organizations need data governance. The results are clear.

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Knowledge Hut

APRIL 20, 2023

Data governance refers to the set of policies, procedures, mix of people and standards that organisations put in place to manage their data assets. It involves establishing a framework for data management that ensures data quality, privacy, security, and compliance with regulatory requirements.

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Precisely

MARCH 13, 2023

Despite that understanding, many organizations lack a clear framework for organizing, managing, and governing their valuable data assets. In many cases, that realization prompts executive leaders to create a data governance program within their company. In many organizations, that simply isn’t the case.

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Cloudera

NOVEMBER 2, 2021

More use cases must be deployed to drive more insight and value; more data needs to be made available to more users. Data governance: three steps to success. It is safe to assume that businesses understand the importance of good data governance. Know what data you have. Better governance for better outcomes.

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Precisely

JULY 19, 2023

Data governance is fast becoming a business imperative. Many top executives and line-of-business managers lack a clear understanding of the benefits of data governance. Data is a valuable organizational asset, yet if an organization isn’t capable of fully utilizing that asset, there can be a substantial opportunity cost.

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Monte Carlo

JANUARY 24, 2024

Data is among your company’s most valuable commodities, but only if you know how to manage it. More data, more access to data, and more regulations mean data governance has become a higher-stakes game. Data Governance Trends The biggest data governance trend isn’t really a trend at all—rather, it’s a state of mind.

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Cloudera

MARCH 3, 2022

Whether the enterprise uses dozens or hundreds of data sources for multi-function analytics, all organizations can run into data governance issues. Bad data governance practices lead to data breaches, lawsuits, and regulatory fines — and no enterprise is immune. . Everyone Fails Data Governance.

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Cloudera

OCTOBER 1, 2022

According to Gartner, by 2023 65% of the world’s population will have their personal data covered under modern privacy regulations. . As a result, growing global compliance and regulations for data are top of mind for enterprises that conduct business worldwide. – From a recent episode of the TWIML AI Podcast.

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Data Engineering Podcast

MAY 20, 2021

Summary Data governance is a phrase that means many different things to many different people. This is because it is actually a concept that encompasses the entire lifecycle of data, across all of the people in an organization who interact with it. data stewards, business glossaries, etc.)

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Cloudera

SEPTEMBER 23, 2022

Should system resources such as CPU or system memory become constrained, this ops team is responsible to correct. In short, just like on-premise deployments, a small team of operaitons personnel are required to successfully deploy and manage this type of data lakehouse deployment. .

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Cloudera

AUGUST 4, 2021

And if data security tops IT concerns, data governance should be their second priority. Not only is it critical to protect data, but data governance is also the foundation for data-driven businesses and maximizing value from data analytics. But it’s still not easy. But it’s still not easy.

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The Modern Data Company

MARCH 7, 2023

Data governance can be a powerful agent in scaling the use and distribution of trusted data throughout the company. If you missed it, make sure to catch up on Part 1 – Data Timeliness. What Is Data Taxonomy? Data that is properly classified, catalogued, and tagged is usually well-governed data.

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Monte Carlo

MAY 24, 2024

The transactional systems we were using couldn’t handle modeling or large scale analytics very well,” said André. When we migrated, a lot of our service accounts for our data systems lost their permissions and started failing,” he said. When we had a pipeline failure our data engineer had to access every single system.

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AltexSoft

MARCH 2, 2023

As the amount of enterprise data continues to surge, businesses are increasingly recognizing the importance of data governance — the framework for managing an organization’s data assets for accuracy, consistency, security, and effective use. Projections show that the data governance market will expand from $1.81

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Knowledge Hut

OCTOBER 4, 2023

Below are the Power BI requirements for the system. Supported operating system: Power BI program can be installed in a device with the following operations systems. Windows Server 2019 Data Centre, server 2019 standard, server 2016 standard, server 2016 datacenter.

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The Modern Data Company

APRIL 20, 2023

What is a data operating system? On the surface, it’s an operating system designed specifically for managing and processing large amounts of data. Modern Layer over Legacy Systems Many data management systems require modernizing legacy systems by decommissioning them entirely or in part.

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Cloudera

MARCH 9, 2021

Governance and the sustainable handling of data is a critical success factor in virtually all organizations. While Cloudera Data Platform (CDP) already supports the entire data lifecycle from ‘Edge to AI’, we at Cloudera are fully aware that enterprises have more systems outside of CDP.

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Data Engineering Podcast

MAY 18, 2024

Summary The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. Want to see Starburst in action?

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databricks

JULY 5, 2023

This blog authored post by Jaison Dominic, Senior Manager, Information Systems at Amgen, and Lakhan Prajapati, Director of Architecture and Engineering at ZS.

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Data Engineering Podcast

MAY 5, 2024

As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability? What are the practical/architectural methods necessary to build more cognitive AI systems?

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Monte Carlo

AUGUST 17, 2022

Over the years, I’ve helped companies of all sizes build and maintain data systems—from my days as a data engineer at Facebook to my current role as an end-to-end data solutions consultant. As a YouTuber and blogger , I’ve connected with data engineers from all over the world. The stakes also get higher.

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Data Engineering Podcast

APRIL 21, 2024

How do you manage the personalization of the AI functionality in your system for each user/team? Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data.

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Data Engineering Podcast

MAY 12, 2024

Summary Building a data platform is a substrantial engineering endeavor. The services and systems need to be kept up to date, but so does the code that controls their behavior. Support Data Engineering Podcast Summary Building a data platform is a substrantial engineering endeavor. Want to see Starburst in action?

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Knowledge Hut

MARCH 7, 2024

Syncing Across Data Sources Once you import data into Big Data platforms you may also realize that data copies migrated from a wide range of sources on different rates and schedules can rapidly get out of the synchronization with the originating system. This itself could be a challenge for a lot of enterprises.

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Data Engineering Podcast

FEBRUARY 18, 2024

What are the pain points that are still prevalent in lakehouse architectures as compared to warehouse or vertically integrated systems? For someone who is interested in building a data lakehouse with Trino and Iceberg, how does that influence their selection of other platform elements?

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Data Engineering Podcast

FEBRUARY 4, 2024

Summary Stream processing systems have long been built with a code-first design, adding SQL as a layer on top of the existing framework. In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable.

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Data Engineering Podcast

JANUARY 7, 2024

What are the experimental methods that you are using to gain understanding in the opportunities and practical limits of those systems? As you strive to push the limits of technical capacity in data systems, how does that impact the usability of the resulting systems?

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Data Engineering Podcast

APRIL 7, 2024

What are the benefits of having a discrete service that offers the business metrics/semantic mappings as opposed to implementing those concepts as part of a more general system? What are the challenges involved in retrofitting a semantic layer into a production data system? dbt, BI, warehouse marts, etc.)

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Data Engineering Weekly

MARCH 17, 2024

It defines AI broadly, including any machine-based system influencing physical or virtual environments, and categorizes AI applications by risk, from banned "Unacceptable Risk AI Systems" to scrutinized "High-Risk AI Systems." Examining the advantages and drawbacks associated with different approaches to embedding analytics. -

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Towards Data Science

JUNE 8, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Why is data quality ownership important? There are downsides to this approach however.

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Data Engineering Podcast

MARCH 3, 2024

What are the skills and systems that need to be in place to effectively execute on an AI program? "AI" What are the steps to take to build confidence in the availability of the data? What are the most interesting, unexpected, or challenging lessons that you have learned while working on powering AI systems?

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Data Engineering Podcast

DECEMBER 24, 2023

Summary Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data.

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Data Engineering Podcast

APRIL 28, 2024

Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place.

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Monte Carlo

JUNE 6, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Table of Contents Why is important to answer who is responsible for data quality?

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