O'Reilly on Data

FEBRUARY 11, 2020

They don’t have the resources they need to clean up data quality problems. The building blocks of data governance are often lacking within organizations. These include the basics, such as metadata creation and management, data provenance, data lineage, and other essentials. An additional 7% are data engineers.

Data Quality 299

Data Quality Metadata Data Governance Publishing 299

CIO Business Intelligence

MARCH 9, 2022

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

Data Lake 95

Data Lake Unstructured Data Data Warehouse Data Quality 95

CIO Business Intelligence

JUNE 14, 2023

Additionally, it is vital to be able to execute computing operations on the 1000+ PB within a multi-parallel processing distributed system, considering that the data remains dynamic, constantly undergoing updates, deletions, movements, and growth.

Unstructured Data 62

Unstructured Data IT Manufacturing Visualization 62

IBM Big Data Hub

JANUARY 17, 2023

These new technologies and approaches, along with the desire to reduce data duplication and complex ETL pipelines, have resulted in a new architectural data platform approach known as the data lakehouse – offering the flexibility of a data lake with the performance and structure of a data warehouse.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Cloudera

SEPTEMBER 9, 2022

Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested. Imagine independently discovering rich new business insights from both structured and unstructured data working together, without having to beg for data sets to be made available.

Metadata 100

Metadata Machine Learning Unstructured Data Data Lake 100

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. BTW, videos for Rev2 are up: [link]. On deck this time ’round the Moon: program synthesis.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Cloudera

JUNE 7, 2022

In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

IT 110

IT Data Architecture Unstructured Data Big Data 110

Domino Data Lab

JANUARY 21, 2021

This blog explores the challenges associated with doing such work manually, discusses the benefits of using Pandas Profiling software to automate and standardize the process, and touches on the limitations of such tools in their ability to completely subsume the core tasks required of data science professionals and statistical researchers.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

Alation

DECEMBER 15, 2021

A data governance strategy helps prevent your organization from having “bad data” — and the poor decisions that may result! Here’s why organizations need a governance strategy: Makes data available: So people can easily find and use both structured and unstructured data. Choose a Metadata Storage Option.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

IBM Big Data Hub

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Perform data quality monitoring based on pre-configured rules.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

CIO Business Intelligence

JUNE 23, 2022

In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB. But this is not your grandfather’s big data.

IT 72

IT Data Architecture Unstructured Data Big Data 72

Andrew White

DECEMBER 9, 2019

The client had recently engaged with a well-known consulting company that had recommended a large data catalog effort to collect all enterprise metadata to help identify all data and business issues. Modern data (and analytics) governance does not necessarily need: Wall-to-wall discovery of your data and metadata.

Analytics 66

Analytics Data Lake Data Governance Metadata 66

AWS Big Data

AUGUST 15, 2023

We create an S3 bucket to store data that exceeds the Lambda function’s response size limits. The Google Cloud Platform portion of the architecture contains a few services as well: Google Cloud Storage – A managed service for storing unstructured data. For instructions, refer to Setting up databases and tables in AWS Glue.

Recreation/Entertainment 90

Recreation/Entertainment Unstructured Data Business Intelligence Data-driven 90

AWS Big Data

FEBRUARY 1, 2024

The Common Crawl corpus contains petabytes of data, regularly collected since 2008, and contains raw webpage data, metadata extracts, and text extracts. In addition to determining which dataset should be used, cleansing and processing the data to the fine-tuning’s specific need is required. It is continuously updated.

Metadata 99

Metadata Modeling Data Processing Unstructured Data 99

AWS Big Data

MARCH 10, 2023

Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats. However, as data processing at scale solutions grow, organizations need to build more and more features on top of their data lakes.

Data Lake 105

Data Lake Sales Data Warehouse Snapshot 105

AWS Big Data

FEBRUARY 21, 2023

The data vault approach is a method and architectural framework for providing a business with data analytics services to support business intelligence, data warehousing, analytics, and data science needs. Amazon Redshift RA3 instances and Amazon Redshift Serverless are perfect choices for a data vault.

Data Warehouse 67

Data Warehouse Data Lake Cost-Benefit Modeling 67

Cloudera

SEPTEMBER 6, 2018

Support machine learning (ML) algorithms and data science activities, to help with name matching, risk scoring, link analysis, anomaly detection, and transaction monitoring. Provide audit and data lineage information to facilitate regulatory reviews. Spark also enables data science at scale.

Machine Learning 40

Machine Learning Big Data Risk Unstructured Data 40

Cloudera

AUGUST 30, 2018

How do I enable self-service for my rapidly growing data science teams? How do I get to the next level in the data-driven journey fast enough? Below are a few C6 highlights to get you started: A new release of Cloudera’s Altus Director – the tool that helps you spin up multiple data and compute clusters in the cloud.

Enterprise 40

Enterprise Data-driven Digital Transformation Machine Learning 40

Cloudera

AUGUST 21, 2020

Perhaps one of the most significant contributions in data technology advancement has been the advent of “Big Data” platforms. Historically these highly specialized platforms were deployed on-prem in private data centers to ensure greater control , security, and compliance. Streaming data analytics. .

Cost-Benefit 74

Cost-Benefit Big Data ROI Risk 74

Rita Sallam

JULY 22, 2016

By contrast, traditional BI platforms are designed to support modular development of IT-produced analytic content, specialized tools and skills, and significant upfront data modeling, coupled with a predefined metadata layer, is required to access their analytic capabilities. Research VP, Business Analytics and Data Science.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40