AWS Big Data

APRIL 3, 2024

Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time. Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more.

Data Lake 127

Data Lake Snapshot Metadata Data Architecture 127

CIO Business Intelligence

OCTOBER 26, 2023

It’s a role that combines hard skills such as programming, data modeling, and statistics with soft skills such as communication, analytical thinking, and problem-solving. Business intelligence analyst resume Resume-writing is a unique experience, but you can help demystify the process by looking at sample resumes.

Business Intelligence 126

Business Intelligence Data-driven Statistics Data Warehouse 126

erwin

APRIL 29, 2020

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. The CEO also makes decisions based on performance and growth statistics. Who are the data owners?

Key Performance Indicator 111

Key Performance Indicator Metadata Data Governance Data Quality 111

erwin

NOVEMBER 6, 2020

To better explain our vision for automating data governance, let’s look at some of the different aspects of how the erwin Data Intelligence Suite (erwin DI) incorporates automation. Data Cataloging: Catalog and sync metadata with data management and governance artifacts according to business requirements in real time.

Data Governance 97

Data Governance Metadata Data-driven Visualization 97

O'Reilly on Data

MARCH 20, 2019

Data integrity constraints: Many databases don’t allow for strange or unrealistic combinations of input variables and this could potentially thwart watermarking attacks. Applying data integrity constraints on live, incoming data streams could have the same benefits. Disparate impact analysis: see section 1.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

datapine

MAY 29, 2019

Each of that component has its own purpose that we will discuss in more detail while concentrating on data warehousing. A solid BI architecture framework consists of: Collection of data. Data integration. Storage of data. Data analysis. Distribution of data. Data integration.

Business Intelligence 196

Business Intelligence Data Warehouse Dashboards Visualization 196

CIO Business Intelligence

JUNE 9, 2022

In these instances, data feeds come largely from various advertising channels, and the reports they generate are designed to help marketers spend wisely. Others aim simply to manage the collection and integration of data, leaving the analysis and presentation work to other tools that specialize in data science and statistics.

Management 144

Management Advertising Data Lake Sales 144

AWS Big Data

NOVEMBER 29, 2023

Use one click to access your data lake tables using auto-mounted AWS Glue data catalogs on Amazon Redshift for a simplified experience. Learn more about the zero-ETL integrations, data lake performance enhancements, and other announcements below.

Data Warehouse 130

Data Warehouse Analytics Data Lake Machine Learning 130

AWS Big Data

AUGUST 31, 2023

Iceberg stores the metadata pointer for all the metadata files. When a SELECT query is reading an Iceberg table, the query engine first goes to the Iceberg catalog, then retrieves the entry of the location of the latest metadata file, as shown in the following diagram.

Data Lake 119

Data Lake Data Warehouse Metadata Data Architecture 119

Wayne Yaddow

MARCH 5, 2025

First, we look at how unit and integration tests uncover transformation errors at an early stage. Then, we validate the schema and metadata to ensure structural and type consistency and use golden or reference datasets to compare outputs to a recognized standard. Key Tools & Processes Data profiling tools (e.g.,

Testing 52

Testing Data Transformation Statistics Metadata 52

IBM Big Data Hub

JANUARY 9, 2024

High variance in a model may indicate the model works with training data but be inadequate for real-world industry use cases. Limited data scope and non-representative answers: When data sources are restrictive, homogeneous or contain mistaken duplicates, statistical errors like sampling bias can skew all results.

Enterprise 71

Enterprise Data Integration Data Quality Contextual Data 71

Smarten

JANUARY 30, 2024

Business users cannot even hope to prepare data for analytics – at least not without the right tools. Gartner predicts that, ‘data preparation will be utilized in more than 70% of new data integration projects for analytics and data science.’ So, why is there so much attention paid to the task of data preparation?

Analytics 67

Analytics Visualization Data Quality Metadata 67

IBM Big Data Hub

JULY 6, 2022

To help companies avoid that pitfall, IBM has recently announced the acquisition of Databand.ai, a leading provider of data observability solutions. The data observability difference . starts at the data source, collecting data pipeline metadata across key solutions in the modern data stack like Airflow, dbt, Databricks and many more.

Metadata 56

Metadata Data Quality Snapshot Cost-Benefit 56

CIO Business Intelligence

SEPTEMBER 22, 2023

What are the benefits of data management platforms? Modern, data-driven marketing teams must navigate a web of connected data sources and formats. Others aim simply to manage the collection and integration of data, leaving the analysis and presentation work to other tools that specialize in data science and statistics.

Management 105

Management Advertising Data Lake Sales 105

IBM Big Data Hub

NOVEMBER 4, 2022

Increasingly enterprise data is spread across multiple environments which contributes to inconsistent data silos that complicate data governance initiatives and create data integrity issues that could impact Business Intelligence and analytics applications. IBM’s holistic approach to Data Quality.

Data Quality 56

Data Quality Metadata Data Governance Data-driven 56

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 Data-driven Metrics 52

Alation

JUNE 2, 2022

All Machine Learning uses “algorithms,” many of which are no different from those used by statisticians and data scientists. The difference between traditional statistical, probabilistic, and stochastic modeling and ML is mainly in computation. Recently, Judea Pearl said, “All ML is just curve fitting.” Conclusion.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. If you want more control over and more value from all your data, join us for a demo of erwin MM.

Data Governance 40

Data Governance Risk Metadata Management 40

Ontotext

JULY 11, 2019

Ontotext’s GraphDB is an enterprise-ready semantic graph database (also called RDF triplestore because it stores data in RDF triples). It provides the core infrastructure for solutions where modelling agility, data integration, relationship exploration, cross-enterprise data publishing and consumption are critical. .

Statistics 40

Statistics Modeling Metadata Enterprise 40

Andrew White

JANUARY 9, 2022

We found anecdotal data that suggested things such as a) CDO’s with a business, more than a technical, background tend to be more effective or successful, and b) CDOs most often came from a business background, and c) those that were successful had a good chance at becoming CEO or CEO or some other CXO (but not really CIO).

Analytics 64

Analytics Measurement Data-driven Modeling 64

Jet Global

MAY 1, 2023

Some cloud applications can even provide new benchmarks based on customer data. Advanced Analytics Some apps provide a unique value proposition through the development of advanced (and often proprietary) statistical models. Advanced Analytics Provide the unique benefit of advanced (and often proprietary) statistical models in your app.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Ontotext

NOVEMBER 11, 2024

Knowledge graphs, while not as well-known as other data management offerings, are a proven dynamic and scalable solution for addressing enterprise data management requirements across several verticals. This often leaves business insights and opportunities lost among a tangled complexity of meaningless, siloed data and content.

Metadata 52

Metadata Knowledge Discovery Data Integration Management 52

DataKitchen

JANUARY 23, 2023

Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements. Data testing can be done through various methods, such as data profiling, Statistical Process Control, and quality checks.

Testing 130

Testing Data Governance Data Quality Data-driven 130