Cloudera

MARCH 9, 2021

In this blog, we’ll highlight the key CDP aspects that provide data governance and lineage and show how they can be extended to incorporate metadata for non-CDP systems from across the enterprise. The SDX layer of CDP leverages the full spectrum of Atlas to automatically track and control all data assets.

Data Governance 96

Data Governance Government Metadata Datasets 96

Cloudera

OCTOBER 23, 2024

In August, we wrote about how in a future where distributed data architectures are inevitable, unifying and managing operational and business metadata is critical to successfully maximizing the value of data, analytics, and AI.

Metadata 83

Metadata BI Data Lake Business Intelligence 83

phData: Data Engineering

NOVEMBER 1, 2024

In an effort to better understand where data governance is heading, we spoke with top executives from IT, healthcare, and finance to hear their thoughts on the biggest trends, key challenges, and what insights they would recommend. Get the Trendbook What is the Impact of Data Governance on GenAI?

Government 52

Government Data Governance Finance Metadata 52

Precisely

AUGUST 22, 2024

Both architectures tackle significant data management challenges such as integrating disparate data sources, improving data accessibility, automating management processes, and ensuring data governance and security. Problems it solves Data fabric addresses key data management and use challenges.

Data Governance 69

Data Governance Government High Quality Data Metadata 69

Cloudera

APRIL 18, 2024

In those discussions, it was clear that everyone understood the need to treat data estates more cohesively as a whole—that means bringing more attention to security, data governance, and metadata management, the latter of which has become increasingly popular. Learn more about how Cloudera is accelerating enterprise AI.

Metadata 110

Metadata Government Data Governance Data 110

Cloudera

OCTOBER 10, 2024

The open data lakehouse is quickly becoming the standard architecture for unified multifunction analytics on large volumes of data. It combines the flexibility and scalability of data lake storage with the data analytics, data governance, and data management functionality of the data warehouse.

IT 84

IT Data Lake Metadata Data Warehouse 84

Databand.ai

JULY 19, 2023

However, their importance has grown significantly in recent years due to the increasing complexity of data architectures and the growing need for data governance and compliance. In this article: Why Are Data Lineage Tools Important? It provides context for data, making it easier to understand and manage.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Databand.ai

AUGUST 30, 2023

Finally, you should analyze the results of your data performance tests and implement any necessary improvements to your data processing systems and infrastructure. Metadata is data about data, such as data definitions, data lineage, and data quality rules, that helps organizations understand and manage their data more effectively.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Data Engineering Weekly

OCTOBER 6, 2024

link] Medium: Learnings from optimising 22 of our most expensive Snowflake pipelines A blog about real-world learning is always a delight to read. Though the blog talks about Superset, every team should strive to adopt a similar framework to manage the lifecycle of their dashboards. “text-to-SQL” and “text-to-insight.”

Data Engineer 62

Data Engineer Data Engineering Engineering Data Warehouse 62

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. This may include tasks such as data profiling, data cleansing, and metadata management.

Building 52

Building Data Cleanse Data Governance Datasets 52

Data Engineering Weekly

AUGUST 25, 2024

[link] FourSquare: Modern Data Platform: An Unbundling of a Traditional Data Warehouse When building their data platform, companies face a critical decision: adopt an all-in-one solution from vendors like Databricks, Snowflake, or AWS or compose a custom platform using tools from different providers.

Data Engineer 70

Data Engineer Data Engineering Engineering Database-centric 70

Data Science Blog: Data Engineering

MAY 20, 2024

Modelling Approaches : It supports top-down modelling, bottom-up modelling, import from external modelling tool, Dimensional/Kimball, Data Vault 2.0, and Kimball, Inmon, 3NF, or any custom data model. Versioning: AnalyticsCreator maintains a version of history of metadata changes. Mixed approach of DV 2.0

Data Pipeline 52

Data Pipeline BI Data Lake Data Warehouse 52

Snowflake

JUNE 5, 2024

To help organizations better govern AI, Snowflake Horizon is also advancing security, lineage and sharing capabilities for models. Additional built-in UI’s and privacy enhancements make it even easier to understand and manage sensitive data. Learn more in the blog.

Government 101

Government Accessible Accessibility Cloud 101

Databand.ai

AUGUST 30, 2023

Integrating these principles with data operation-specific requirements creates a more agile atmosphere that supports faster development cycles while maintaining high quality standards. Organizations need to establish data governance policies, processes, and procedures, as well as assign roles and responsibilities for data governance.

Data Governance 40

Data Governance Data Cleanse Government Data Science 40

Databand.ai

AUGUST 30, 2023

Poor data quality: The lack of automation and data governance in legacy architectures can lead to data quality issues, such as incomplete, inaccurate, or duplicate data. This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

phData: Data Engineering

SEPTEMBER 19, 2023

By employing robust data modeling techniques, businesses can unlock the true value of their data lake and transform it into a strategic asset. With many data modeling methodologies and processes available, choosing the right approach can be daunting. Want to learn more about data governance?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Cloudera

AUGUST 10, 2021

SDX enhancements for improved platform and data governance, including the following notable features: . We expand on this feature later in this blog. x, as well as documented rollback procedures to help customers with their move to CDP PvC Base, as mentioned in the blog introduction. . x, and 6.3.x, x, and 6.3.x,

Cloud 96

Cloud Kafka Metadata SQL 96

Cloudera

JUNE 7, 2023

Agencies should inventory, categorize, and label data; protect data at rest and in transit; and deploy mechanisms to detect and stop data exfiltration. Agencies should carefully craft and review data governance policies to ensure all data lifecycle security aspects are appropriately enforced across the enterprise.”

Government 82

Government Metadata Data Lake Data 82

Snowflake

APRIL 29, 2024

Additionally, data can be classified and tagged based on its level of sensitivity, confidentiality or importance to the organization. This prioritizes security measures and simplifies data discovery. Data Governance: Snowflake also offers a comprehensive list of data governance features.

Transportation 85

Transportation Data Governance Government Consulting 85

Cloudera

MARCH 22, 2023

Luke Roquet recently drilled into the topic of data observability with Mark Ramsey of Ramsey International (RI) to also cover the five pillars (freshness, distribution, volume, schema, and lineage) that describe the quality and reliability of data. And, crucial for a hybrid data platform, it does so across hybrid cloud.

Data Governance 87

Data Governance Government Business Analyst Metadata 87

phData: Data Engineering

DECEMBER 16, 2021

Welcome to part two of our trilogy on data catalogs. If you missed our first blog on what a data catalog is , be sure to check it out! In this blog, we’ll explore what the ideal team to support your data catalog looks like. Who Are the Users of a Data Catalog?

Metadata 52

Metadata Datasets Data Education 52

Cloudera

DECEMBER 20, 2021

Understanding that the future of banking is data-driven and cloud-based, Bank of the West embraced cloud computing and its benefits, like remote capabilities, integrated processes, and flexible systems. The platform is centralizing the data, data management & governance, and building custom controls for data ingestion into the system.

Government 90

Government Data Security Banking Metadata 90

Scott Logic

AUGUST 8, 2024

In this post, I’ll look at some of those challenges across the data lifecycle, and share insights from our recent work and conversations with government about practical ways forward. I’ll begin at the point in the lifecycle where data informs decision-making and service delivery.

Government 52

Government Algorithm Data Governance Metadata 52

Cloudera

MAY 2, 2022

from China to the UK , new data governance and protection rules are coming in on an almost daily basis. This is not just to implement specific governance rules — such as tagging, metadata management, access controls, or anonymization — but to prepare for the potential for rules to change in the future. .

Cloud 103

Cloud Government Healthcare Metadata 103

Databand.ai

AUGUST 30, 2023

Data Cataloging Data cataloging is the process of organizing and categorizing data assets to make them easily discoverable and understandable for data teams. This enables data teams to quickly and easily find the data they need for their analytics projects. In this article: Why Are DataOps Tools Important?

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Cloudera

AUGUST 3, 2021

Advanced analytics and enterprise data are empowering several overarching initiatives in supply chain risk reduction – improved visibility and transparency into all aspects of the supply chain balanced with data governance and security. . Improve Visibility within Supply Chains.

Manufacturing 111

Manufacturing Retail Government Entertainment 111

Cloudera

JUNE 29, 2023

To improve the way they model and manage risk, institutions must modernize their data management and data governance practices. Implementing a modern data architecture makes it possible for financial institutions to break down legacy data silos, simplifying data management, governance, and integration — and driving down costs.

Data Architecture 85

Data Architecture Architecture Management Banking 85

DataKitchen

FEBRUARY 2, 2023

A combined, interoperable suite of tools for data team productivity, governance, and security for large and small data teams. And the tools for acting on data are consolidating: Tableau does data prep, Altreyx does data science, Qlik joined with Talend, etc. And their business customers want more data trust.

Metadata 52

Metadata BI Government Data Science 52

Cloudera

MARCH 23, 2022

The table information (such as schema, partition) is stored as part of the metadata (manifest) file separately, making it easier for applications to quickly integrate with the tables and the storage formats of their choice. Enterprise grade security and data governance – centralized data authorization to lineage and auditing.

Metadata 91

Metadata Data Architecture BI Machine Learning 91

Snowflake

JUNE 4, 2024

New AI governance advancements help organizations better understand, share and secure models. Governed internal collaboration with better discoverability and AI-powered object metadata Snowflake is introducing an entirely new way for data teams to easily discover, curate and share data, apps and now also models (private preview soon).

Government 84

Government Data Ingestion Data PostgreSQL 84

DataKitchen

AUGUST 16, 2024

At DataKitchen, we think of this is a ‘meta-orchestration’ of the code and tools acting upon the data. Data Pipeline Observability: Optimizes pipelines by monitoring data quality, detecting issues, tracing data lineage, and identifying anomalies using live and historical metadata.

Data Pipeline 50

Data Pipeline Metadata Data Governance Coding 50

AltexSoft

FEBRUARY 11, 2023

If you are not familiar with the above-mentioned concepts, we suggest you to follow the links above to learn more about each of them in our blog posts. Bad data management be like, Source: Makeameme Data architects are sometimes confused with other roles inside the data science team.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Databand.ai

DECEMBER 29, 2022

He has built everything from feature stores, experimentation platforms, metrics layers, and streaming platforms to analytics tools, data discovery systems, and workflow development platforms, and he believes that applying product thinking to holistic data challenges is the only way to make trustworthy decisions at scale.

BI 52

BI Consulting Data Science Data Governance 52

Towards Data Science

MAY 19, 2023

Tech Target , defines a data silo as a repository of data controlled by one department or business unit and, therefore, not wholly or easily accessible by other departments within the same organisation. Absence or poor adoption of company-wide guidelines surrounding the creation and deployment of data products.

Pipeline-centric 104

Pipeline-centric Data Software Engineering Software Engineer 104

Lyft Engineering

OCTOBER 3, 2023

We can also evaluate timeliness of partitions and table landing times in data orchestration. Finally, as the subject of this blog post, we can assess data quality via batch compute analytics on our data warehouse, providing a comprehensive albeit slower evaluation compared to the previously mentioned methods.

Big Data 85

Big Data Metadata Data Warehouse Data 85

Monte Carlo

SEPTEMBER 12, 2022

So Maxime predicts that more organizations will do away with the BI-specific semantics altogether, in favor of containing this metadata within the transform layer. There’s an incentive to move semantics, business rules, and nomenclature into the transform layer of the data stack,” says Maxime. Maxime says.

BI 52

BI Data Governance ETL Tools Data Warehouse 52

phData: Data Engineering

DECEMBER 16, 2021

In our previous blog in this series , we spent a lot of time exploring why a data catalog is valuable and who you might need to support it. With that background information in mind, we’re ready to take a look at some actual tools and properly uncover what’s the best data catalog for your business. Where is your metadata stored?

Metadata 52

Metadata Datasets Cloud ETL Tools 52