IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. After all, Alex may not be aware of all the data available to her.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

DataKitchen

JANUARY 19, 2022

DataKitchen acts as a process hub that unifies tools and pipelines across teams, tools and data centers. DataKitchen could, for example, provide the scaffolding upon which a Snowflake cloud data platform or data warehouse could be integrated into a heterogeneous data mesh domain. Abhinav Tiwari.

Testing 130

Testing Data Integration Data Warehouse Enterprise 130

Ontotext

MARCH 18, 2020

The many data warehouse systems designed in the last 30 years present significant difficulties in that respect. When designing a system’s architecture, it’s impossible to know from the start all the ways data will be used. The third challenge is how to combine data management with analytics.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

Alation

DECEMBER 15, 2022

March 2015: Alation emerges from stealth mode to launch the first official data catalog to empower people in enterprises to easily find, understand, govern and use data for informed decision making that supports the business. May 2016: Alation named a Gartner Cool Vendor in their Data Integration and Data Quality, 2016 report.

Metadata 52

Metadata Data Governance Data Quality Marketing 52

Ontotext

MAY 2, 2024

Storage-centric approach In the storage-centric approach, people try to address data silos by throwing everything in a data lake or a data warehouse. But, although, this helps somewhat in terms of architecture, soon these data lakes become unwieldy.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

Alation

FEBRUARY 13, 2020

Supplier metadata is especially important for data acquired from external sources, informing about sources and subscription or licensing constraints. I’ll dive deeper into catalog metadata in an upcoming blog. What is a Data Catalog? What Does a Data Catalog Do? Benefits of a Data Catalog. Conclusion.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

Ontotext

MARCH 8, 2023

Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. Several factors are driving the adoption of knowledge graphs.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case. Make sure that the value of data increases over time.

Data Governance 52

Data Governance Strategy Data Quality Marketing 52

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. This blog post provides a concise session summary, a video, and a written transcript. data science’s emergence as an interdisciplinary field – from industry, not academia. But for most enterprise, using machine learning…not really.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

Testing 300

Testing Machine Learning Consulting Data Quality 300

DataKitchen

SEPTEMBER 21, 2023

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Cloudera

DECEMBER 15, 2022

With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback. Read why the future of data lakehouses is open.

Metadata 82

Metadata Data Warehouse Snapshot Data Quality 82

Cloudera

AUGUST 30, 2022

For state and local agencies, data silos create compounding problems: Inaccessible or hard-to-access data creates barriers to data-driven decision making. Legacy data sharing involves proliferating copies of data, creating data management, and security challenges. Towards Data Science ). Forrester ).

Data Architecture 82

Data Architecture Data Lake Metadata Data Warehouse 82

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

IBM Big Data Hub

DECEMBER 7, 2023

Today, OLAP database systems have become comprehensive and integrated data analytics platforms, addressing the diverse needs of modern businesses. They are seamlessly integrated with cloud-based data warehouses, facilitating the collection, storage and analysis of data from various sources.

OLAP 58

OLAP Slice and Dice Cost-Benefit Data Warehouse 58

Jet Global

JULY 11, 2019

With major advances being made in artificial intelligence and machine learning, businesses are investing heavily in advanced analytics to get ahead of the competition and increase their bottom line. Data Consolidation. Learn more about data warehouses here. Data Analysis. Read the Blog Post.

Business Intelligence 72

Business Intelligence Forecasting Data Warehouse Predictive Analytics 72

Alation

AUGUST 11, 2022

The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Subscribe to Alation's Blog.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

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 80

Management Metadata Data Architecture Data Lake 80

Alation

OCTOBER 21, 2021

Just as lakes benefit from the filtering power of surrounding rocks, roots, and soil to sift out incoming impurities, data lakes benefit from a diligent effort to prevent them from becoming a dumping ground for all and any data. Ungoverned data. Data governance helps keep data quality high and data literacy efforts on track.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Cloudera

SEPTEMBER 9, 2022

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.” According to Gartner, Inc.

Metadata 105

Metadata Machine Learning Unstructured Data Data Lake 105

Cloudera

APRIL 14, 2021

Refer to the following cloudera blog to understand the full potential of Cloudera Data Engineering. . Precisely Data Integration, Change Data Capture and Data Quality tools support CDP Public Cloud as well as CDP Private Cloud. Why should technology partners care about CDE? References: [link].

Data Warehouse 86

Data Warehouse Data Processing Data Quality Machine Learning 86

Jet Global

OCTOBER 1, 2019

With major advances being made in artificial intelligence and machine learning, businesses are investing heavily in advanced analytics to get ahead of the competition and increase their bottom line. Data Consolidation. Learn more about data warehouses here. Data Analysis. Read the Blog Post.

Business Intelligence 40

Business Intelligence Forecasting Data Warehouse Predictive Analytics 40

IBM Big Data Hub

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

Cost-Benefit 76

Cost-Benefit Metadata Optimization Management 76

Domino Data Lab

JUNE 15, 2021

At a certain point, as the demand keeps growing, the data volumes rapidly increase. Data is no longer stored in CSV files, but in a dedicated, purpose built data lake / data warehouse. In this blog post we’ll focus on the UDF capabilities provided by the two platforms. Why Snowflake UDFs.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

IBM Big Data Hub

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Sometimes, these outputs are biased because the data used to train the model was incomplete or inaccurate in some way.

Cost-Benefit 77

Cost-Benefit Metadata Data Governance Modeling 77

Alation

JANUARY 25, 2022

Data fabric is now on the minds of most data management leaders. In our previous blog, Data Mesh vs. Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. The data catalog is a foundational layer of the data fabric. Spoiler alert!

Metadata 52

Metadata IT Metrics Data-driven 52

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams. Friction ensued.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

datapine

AUGUST 29, 2022

James Warren, on the other part, is a successful analytics architect with a background in machine learning and scientific computing. 5) Data Analytics Made Accessible, by Dr. Anil Maheshwari. Best for : the new intern who has no idea what data science even means.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Alation

AUGUST 11, 2021

Anthony and his governance team use data to uncover customer insights, and to discover areas where they can improve safety. Like many organizations, TMIC had a complex set of data sources and internal data warehouses. Data quality and stewardship for effective cloud governance. Subscribe to Alation's Blog.

Data Governance 52

Data Governance Insurance Metadata Strategy 52

Alation

FEBRUARY 13, 2020

Data is not something that’s easy to consume, it’s not something easy to recognize. We need more that makes it easy to identify data quality, data issues, et cetera. I’m about to query a table that is deprecated or has a data quality issue. That’s as I’m creating a data asset.

Dashboards 52

Dashboards Data-driven Visualization Data Quality 52

Cloudera

AUGUST 26, 2020

Gluent’s Smart Connector is capable of pushing processing to Cloudera, thereby reducing the storage and compute footprint on traditional data warehouses like Oracle. This allows our customers to reduce spend on highly specialized hardware and leverage the tools of a modern data warehouse. . Certified Data Quality Partner.

Machine Learning 85

Machine Learning Big Data Data Warehouse Data-driven 85

Alation

AUGUST 4, 2022

Many CIOs argue the rise of big data pushed people to use data more proactively for business decision-making. Big data got“ more leaders and people in the organization to use data, analytics, and machine learning in their decision making,” says former CIO Isaac Sacolick. Subscribe to Alation's Blog.

Big Data 95

Big Data Digital Transformation Data Lake Data-driven 95

Ontotext

AUGUST 4, 2023

This logical data architecture is designed to help organizations deal with growing volumes of data, spanning data silos with seamless connectivity and a knowledge layer. Using metadata, machine learning (ML), and automation, a data fabric provides a unified view of enterprise data across data formats and locations.

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. MLOps creates a process where it’s easier to cull insights from business data.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

Cloudera

APRIL 1, 2024

It unifies structured data with unstructured data giving a holistic view of the business and enhancing the efficacy of AI by providing it with more structured and accessible business data as context. LLMs in our Cloudera Machine Learning (CML) service intelligently assist you in data science and AI projects.

Unstructured Data 108

Unstructured Data Cost-Benefit Metadata Machine Learning 108

IBM Big Data Hub

JULY 27, 2023

Data quality strongly impacts the quality and usefulness of content produced by an AI model, underscoring the significance of addressing data challenges. It provides the combination of data lake flexibility and data warehouse performance to help to scale AI.

Management 54

Management Enterprise Modeling Data Quality 54

Alation

JANUARY 27, 2022

The proliferation of data sources means there is an increase in data volume that must be analyzed. Large volumes of data have led to the development of data lakes , data warehouses, and data management systems. Despite its immense value, a variety of data can create more work.

Data Governance 52

Data Governance Analytics Cost-Benefit Data-driven 52