IBM Big Data Hub

JULY 28, 2022

Metadata enrichment is about scaling the onboarding of new data into a governed data landscape by taking data and applying the appropriate business terms, data classes and quality assessments so it can be discovered, governed and utilized effectively. Scalability and elasticity.

Metadata 56

Metadata Data Quality Machine Learning Statistics 56

AWS Big Data

APRIL 29, 2024

The construction of big data applications based on open source software has become increasingly uncomplicated since the advent of projects like Data on EKS , an open source project from AWS to provide blueprints for building data and machine learning (ML) applications on Amazon Elastic Kubernetes Service (Amazon EKS).

Management 98

Management Big Data Metadata Data Warehouse 98

AWS Big Data

FEBRUARY 6, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development. MongoDB Atlas is a developer data service from AWS technology partner MongoDB, Inc.

Metadata 77

Metadata Data Lake Machine Learning Management 77

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 96

Data Lake Snapshot Metadata Data Architecture 96

AWS Big Data

OCTOBER 25, 2023

By some estimates, unstructured data can make up to 80–90% of all new enterprise data and is growing many times faster than structured data. After decades of digitizing everything in your enterprise, you may have an enormous amount of data, but with dormant value. The solution integrates data in three tiers.

Unstructured Data 121

Unstructured Data Metadata Management Analytics 121

Rocket-Powered Data Science

JULY 6, 2021

This is accomplished through tags, annotations, and metadata (TAM). My favorite approach to TAM creation and to modern data management in general is AI and machine learning (ML). Smart content includes labeled (tagged, annotated) metadata (TAM). Tagging and annotating those subcomponents and subsets (i.e.,

Strategy 267

Strategy Machine Learning Metadata Knowledge Discovery 267

Alation

FEBRUARY 13, 2020

In an earlier blog, I defined a data catalog as “a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses.”.

Metadata 52

Metadata Management Data Lake Data Governance 52

Octopai

APRIL 19, 2021

Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

AWS Big Data

MAY 7, 2024

Figure 1: Flow of actions for self-service analytics around data assets stored in relational databases First, the data producer needs to capture and catalog the technical metadata of the data asset. The producer also needs to manage and publish the data asset so it’s discoverable throughout the organization.

Metadata 77

Metadata Data Lake Data Processing Data-driven 77

IBM Big Data Hub

OCTOBER 4, 2023

Also, a lakehouse can introduce definitional metadata to ensure clarity and consistency, which enables more trustworthy, governed data. And AI, both supervised and unsupervised machine learning, is often the best or sometimes only way to unlock these new big data insights at scale.

Data Lake 80

Data Lake Metadata Cost-Benefit Data Warehouse 80

CIO Business Intelligence

OCTOBER 13, 2023

Information/data governance architect: These individuals establish and enforce data governance policies and procedures. Analytics/data science architect: These data architects design and implement data architecture supporting advanced analytics and data science applications, including machine learning and artificial intelligence.

Data Architecture 130

Data Architecture Data Warehouse Statistics Visualization 130

AWS Big Data

MARCH 4, 2024

Apache Iceberg manages these schema changes in a backward-compatible way through its innovative metadata table evolution architecture. For instance, an ecommerce marketplace may initially partition order data by day. Lake Formation helps you centrally manage, secure, and globally share data for analytics and machine learning.

Snapshot 114

Snapshot Data Lake Metadata Recreation/Entertainment 114

AWS Big Data

JULY 14, 2023

In this post, we discuss how the Amazon Finance Automation team used AWS Lake Formation and the AWS Glue Data Catalog to build a data mesh architecture that simplified data governance at scale and provided seamless data access for analytics, AI, and machine learning (ML) use cases.

Finance 86

Finance Metadata Big Data Recreation/Entertainment 86

AWS Big Data

JANUARY 16, 2024

The need for Amazon MWAA disaster recovery Amazon MWAA, a fully managed service for Apache Airflow , brings immense value to organizations by automating workflow orchestration for extract, transform, and load (ETL), DevOps, and machine learning (ML) workloads. This makes it difficult to implement a comprehensive DR strategy.

Strategy 102

Strategy Metadata Metrics Dashboards 102

AWS Big Data

APRIL 2, 2024

Data consumers need detailed descriptions of the business context of a data asset and documentation about its recommended use cases to quickly identify the relevant data for their intended use case. Go to your asset in your data project and choose Generate summary to obtain the detailed description of the asset and its columns.

Metadata 91

Metadata Metrics Data-driven Modeling 91

Rocket-Powered Data Science

JULY 19, 2023

The strategy addresses the “what, when, where, why, and how” questions from business leaders concerning the placement of “sensors” that are used to collect the essential data that power the sentinel analytics product, in order to generate timely insights and thereby enable better data-informed “just in time” business decisions. (b)

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

Rocket-Powered Data Science

MARCH 10, 2020

The book Graph Algorithms: Practical Examples in Apache Spark and Neo4j is aimed at broadening our knowledge and capabilities around these types of graph analyses, including algorithms, concepts, and practical machine learning applications of the algorithms.

Metadata 250

Metadata Machine Learning ROI Prescriptive Analytics 250

Cloudera

FEBRUARY 22, 2022

Over the past decade, the successful deployment of large scale data platforms at our customers has acted as a big data flywheel driving demand to bring in even more data, apply more sophisticated analytics, and on-board many new data practitioners from business analysts to data scientists.

Snapshot 107

Snapshot Metadata Cost-Benefit Data Architecture 107

Cloudera

SEPTEMBER 15, 2022

Manufacturing, where the data they generate can provide new business opportunities like predictive maintenance in addition to improving their operational efficiency. Retail, where big data is used across all stages of the retail process—from product development, pricing, demand forecasting, and for inventory optimization in the stores.

Metadata 87

Metadata Big Data Optimization Unstructured Data 87

Cloudera

APRIL 1, 2024

This recognition underscores Cloudera’s commitment to continuous customer innovation and validates our ability to foresee future data and AI trends, and our strategy in shaping the future of data management. Cloudera, a leader in big data analytics, provides a unified Data Platform for data management, AI, and analytics.

Unstructured Data 104

Unstructured Data Cost-Benefit Metadata Machine Learning 104

AWS Big Data

NOVEMBER 22, 2023

Many AWS customers adopted Apache Hudi on their data lakes built on top of Amazon S3 using AWS Glue , a serverless data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development.

Data Lake 95

Data Lake Snapshot Metadata Optimization 95

Cloudera

JANUARY 22, 2019

In conjunction with the evolving data ecosystem are demands by business for reliable, trustworthy, up-to-date data to enable real-time actionable insights. Big Data Fabric has emerged in response to modern data ecosystem challenges facing today’s enterprises. What is Big Data Fabric? Data access.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

AWS Big Data

JULY 20, 2023

One key component that plays a central role in modern data architectures is the data lake, which allows organizations to store and analyze large amounts of data in a cost-effective manner and run advanced analytics and machine learning (ML) at scale. Why did Orca choose Apache Iceberg?

Data Lake 90

Data Lake Analytics Snapshot Optimization 90

AWS Big Data

DECEMBER 13, 2023

The AWS Glue Studio visual editor is a low-code environment that allows you to compose data transformation workflows, seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine, and inspect the schema and data results in each step of the job.

Metadata 91

Metadata Data Lake Visualization Data Transformation 91

AWS Big Data

APRIL 12, 2023

Amazon EMR on EKS provides a deployment option for Amazon EMR that allows organizations to run open-source big data frameworks on Amazon Elastic Kubernetes Service (Amazon EKS). Also, you can run other types of business applications, such as web applications and machine learning (ML) TensorFlow workloads, on the same EKS cluster.

Testing 77

Testing Big Data Metadata Optimization 77

AWS Big Data

FEBRUARY 29, 2024

The need for an end-to-end strategy for data management and data governance at every step of the journey—from ingesting, storing, and querying data to analyzing, visualizing, and running artificial intelligence (AI) and machine learning (ML) models—continues to be of paramount importance for enterprises.

Data Governance 97

Data Governance Unstructured Data Metadata Data Lake 97

AWS Big Data

NOVEMBER 15, 2023

It seamlessly consolidates data from various data sources within AWS, including AWS Cost Explorer (and forecasting with Cost Explorer ), AWS Trusted Advisor , and AWS Compute Optimizer. They can use their own toolsets or rely on provided blueprints to ingest the data from source systems.

Dashboards 97

Dashboards Analytics Metadata Data Warehouse 97

AWS Big Data

DECEMBER 18, 2023

Customers use Amazon Redshift as a key component of their data architecture to drive use cases from typical dashboarding to self-service analytics, real-time analytics, machine learning (ML), data sharing and monetization, and more.

Data Warehouse 100

Data Warehouse Machine Learning Data-driven Data Lake 100

Smart Data Collective

APRIL 5, 2022

They are using tools like Amazon SageMaker to take advantage of more powerful machine learning capabilities. Amazon SageMaker is a hardware accelerator platform that uses cloud-based machine learning technology. IBM Watson Studio is a very popular solution for handling machine learning and data science tasks.

Cost-Benefit 123

Cost-Benefit Machine Learning Data Science Unstructured Data 123

AWS Big Data

NOVEMBER 17, 2023

DynamoDB offers built-in security, continuous backups, automated multi-Region replication, in-memory caching, and data import and export tools. The scalability and flexible data schema of DynamoDB make it well-suited for a variety of use cases. Data stored in DynamoDB is the basis for valuable business intelligence (BI) insights.

Visualization 85

Visualization Metadata Testing Internet of Things 85

FineReport

MAY 6, 2020

Menurut saya, data analyst nampaknya cuma menganalisis data bisnis dan saya tidak tahu bagaimana cara meningkatkan skill saya.” Ini karena dia tidak sepenuhnya menggali nilai dari analisis big data. Software Pemvisualisasi Data: excel, python, software profesional lainnya. Data Warehous: SSIS, SSAS.

Data mining 98

Data mining Data Warehouse Machine Learning Big Data 98

Alation

FEBRUARY 13, 2020

Key analyst firms like Forrester, Gartner, and 451 Research have cited “ soaring demands from data catalogs ”, pondered whether data catalogs are the “ most important breakthrough in analytics to have emerged in the last decade ,” and heralded the arrival of a brand new market: Machine Learning Data Catalogs.

Metadata 52

Metadata Machine Learning Data Governance Reporting 52

Alation

FEBRUARY 13, 2020

Why do we need a data catalog? What does a data catalog do? These are all good questions and a logical place to start your data cataloging journey. Data catalogs have become the standard for metadata management in the age of big data and self-service analytics. What is a Data Catalog?

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

CIO Business Intelligence

DECEMBER 13, 2023

Unlike that energy company, many organizations have yet to feel an urgency to capitalize on the value of their vast reservoirs of unstructured data. After all, we in the information management and technology industry have talked at length about unstructured data since “Big Data” was big news more than a decade ago.

Unstructured Data 97

Unstructured Data IoT Metadata Manufacturing 97

CIO Business Intelligence

MARCH 21, 2022

Data scientists are becoming increasingly important in business, as organizations rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies. Data scientist job description. Semi-structured data falls between the two.

Unstructured Data 135

Unstructured Data Data Analytics Analytics Structured Data 135

CIO Business Intelligence

MARCH 24, 2023

The program must introduce and support standardization of enterprise data. Programs must support proactive and reactive change management activities for reference data values and the structure/use of master data and metadata.

Data Governance 142

Data Governance Management Metadata Data Quality 142

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake 107

Data Lake Unstructured Data Data Warehouse Data Quality 107