Cloudera

AUGUST 31, 2021

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

Data Warehouse 85

Data Warehouse Cost-Benefit Metadata Data-driven 85

Cloudera

JUNE 18, 2022

These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Data Lake 96

Data Lake Data Warehouse Machine Learning Cost-Benefit 96

AWS Big Data

MARCH 2, 2023

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback.

Data Lake 119

Data Lake Data Processing Metadata Snapshot 119

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

Cloudera

JULY 14, 2023

Introduction For more than a decade now, the Hive table format has been a ubiquitous presence in the big data ecosystem, managing petabytes of data with remarkable efficiency and scale. Depending on the size and usage patterns of the data, several different strategies could be pursued to achieve a successful migration.

Snapshot 72

Snapshot Metadata Data Warehouse Testing 72

IBM Big Data Hub

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

IBM Big Data Hub

APRIL 28, 2022

A data fabric can simplify data access in an organization to facilitate self-service data consumption, while remaining agnostic to data environments, processes, utility and geography. Obtaining access to each data warehouse and subsequently drawing relationships between the data would be a cumbersome process.

Metadata 78

Metadata Data Warehouse Forecasting Predictive Modeling 78

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. Technical metadata to describe schemas, indexes and other database objects.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

Risk 71

Risk Modeling Management Metadata 71

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

Alation

JULY 22, 2021

This course called on the students to utilize the catalog to find and query sample data, and then to publish results into articles on the site. For the course, ‘Big Data and Society’, we loaded publicly available COVID-19 data into the catalog for student use and investigation. iSchool Skills for Data Catalog Management.

Informatics 98

Informatics Big Data Insurance Metadata 98

datapine

SEPTEMBER 29, 2022

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. 2 – Data profiling. Data profiling is an essential process in the DQM lifecycle.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Alation

FEBRUARY 20, 2020

We define it as this: Data acquisition is the processes for bringing data that has been created by a source outside the organization, into the organization, for production use. Prior to the Big Data revolution, companies were inward-looking in terms of data. THE NEED FOR METADATA TOOLS.

Metadata 52

Metadata IT Data Governance Data Warehouse 52

erwin

OCTOBER 31, 2019

What data do we have and where is it? Data is a critical asset used to operate, manage and grow a business. While sometimes at rest in databases, data lakes and data warehouses; a large percentage is federated and integrated across the enterprise, introducing governance, manageability and risk issues that must be managed.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

erwin

APRIL 27, 2022

In data warehousing, the data is extracted and transported from production database(s) into a data warehouse for reporting and analysis. Change Data Capture identifies and processes only the data that has changed and stores the changed data in a form so as to be of further use.

Data Governance 52

Data Governance Digital Transformation Metadata Data Warehouse 52

Data Virtualization

OCTOBER 22, 2021

Since its first incarnation almost 35 years ago in my IBM Systems Journal article, the data warehouse (DW) has remained a key architectural pattern for decision-making support. The post Weaving Architectural Patterns: I – Data Fabric appeared first on Data Virtualization blog.

Data Warehouse 52

Data Warehouse Reporting IT Metadata 52

Data Virtualization

OCTOBER 21, 2021

Since its first incarnation almost 35 years ago in my IBM Systems Journal article, the data warehouse (DW) has remained a key architectural pattern for decision-making support. The post Weaving Architectural Patterns: I – Data Fabric appeared first on Data Virtualization blog.

Data Warehouse 52

Data Warehouse Reporting IT Metadata 52

erwin

APRIL 2, 2021

And knowing the business purpose translates into actively governing personal data against potential privacy and security violations. Do You Know Where Your Sensitive Data Is? Data is a valuable asset used to operate, manage and grow a business.

Data Governance 106

Data Governance Cost-Benefit Risk Metadata 106

Cloudera

FEBRUARY 11, 2019

The January 2019 “Magic Quadrant for Data Management Solutions for Analytics” provides valuable insights into the status, direction, and players in the DMSA market. In this blog, we share our takeaways as they relate to the DMSA market trends. 3. Expansion beyond core data management. Cloudera’s Positioning.

Management 43

Management Metadata Analytics Machine Learning 43

IBM Big Data Hub

JUNE 8, 2022

A typical organization’s data landscape consists of a large number of data stores across workflows, business processes and business units, including but not limited to data warehouses, data marts, data lakes, ODS, cloud data stores, and CRM databases. The volume of data assets.

Data-driven 56

Data-driven Enterprise Data Governance Data Lake 56

erwin

MARCH 8, 2019

Therefore, the organization needed to catalog the data it acquires from suppliers, ensure its quality, classify it, and then sell it to customers. The company wanted to assemble the data in a data warehouse and then provide controlled access to it.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

AWS Big Data

JANUARY 17, 2024

Incremental query refers to a query strategy that focuses on processing and analyzing only the new or updated data within a data lake since the last query. The key idea behind incremental queries is to use metadata or change tracking mechanisms to identify the new or modified data since the last query.

Data Lake 94

Data Lake Snapshot Big Data Data-driven 94

AWS Big Data

JANUARY 8, 2024

Therefore, it is critical for organizations to embrace a low-latency, scalable, and reliable data streaming infrastructure to deliver real-time business applications and better customer experiences. Using a data stream in the middle provides the advantage of using the time series data in other processes and solutions at the same time.

Analytics 114

Analytics IoT Data-driven Snapshot 114

erwin

FEBRUARY 19, 2021

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. What Is Metadata? Harvest data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

TDAN

MARCH 19, 2019

Various databases, plus one or more data warehouses, have been the state-of-the art data management infrastructure in companies for years. The emergence of various new concepts, technologies, and applications such as Hadoop, Tableau, R, Power BI, or Data Lakes indicate that changes are under way.

Management 84

Management Data Lake Data Warehouse Analytics 84

Cloudera

MAY 22, 2018

With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. save the built model container, along with metadata like who built or deployed it. for the Oracle Big Data Appliance).

Data Science 42

Data Science Snapshot Machine Learning Metadata 42

AWS Big Data

JUNE 6, 2023

This is a guest blog post by Mira Daniels and Sean Whitfield from SumUp. Unless, of course, the rest of their data also resides in the Google Cloud. AWS Glue gave us a cost-efficient option to migrate the data and we further optimized storage cost by pruning cold data.

Analytics 55

Analytics Data Lake Testing Optimization 55

Cloudera

FEBRUARY 8, 2024

Overview This blog post describes support for materialized views for the Iceberg table format. It brings the reliability and simplicity of SQL tables to big data while enabling engines like Hive, Impala, Spark, Trino, Flink, and Presto to work with the same tables at the same time. Starting from the CDW Public Cloud DWX-1.6.1

Snapshot 94

Snapshot Metadata Cost-Benefit Data Warehouse 94

AWS Big Data

AUGUST 31, 2023

Amazon Redshift is a fast, fully managed petabyte-scale cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Amazon Redshift also supports querying nested data with complex data types such as struct, array, and map.

Data Lake 94

Data Lake Data Warehouse Metadata Data Architecture 94

Sisense

MAY 28, 2020

In the Clouds is where we explore the ways cloud-native architecture, cloud data storage, and cloud analytics are changing key industries and business practices, with anecdotes from experts, how-to’s, and more to help your company excel in the cloud era. The world of data is constantly changing and speeding up every day.

Internet of Things 83

Internet of Things Metadata Data-driven Unstructured Data 83

Data Virtualization

MARCH 3, 2021

To remain competitive, organizations must have a data management strategy in place to effectively ingest, store, organize, and analyze data while ensuring that it is. The post Data Management Challenges for the Modern Enterprise appeared first on Data Virtualization blog.

Enterprise 75

Enterprise Management Strategy Data Lake 75

IBM Big Data Hub

JANUARY 17, 2023

A data lakehouse is an emerging data management architecture that improves efficiency and converges data warehouse and data lake capabilities driven by a need to improve efficiency and obtain critical insights faster. Let’s start with why data lakehouses are becoming increasingly important.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

AWS Big Data

OCTOBER 4, 2023

Additionally, data owners and data stewards can make data discovery simpler by adding business context to data while balancing access governance to the data via pre-defined approval workflows in the user interface. The metadata forms types, and asset types can be used as templates for defining your assets.

Metadata 101

Metadata Data Lake Publishing Data Governance 101

AWS Big Data

NOVEMBER 22, 2023

Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance.

Data Lake 94

Data Lake Snapshot Metadata Optimization 94

IBM Big Data Hub

OCTOBER 4, 2023

In fact, according in an IDC DataSphere study, IDC estimated that 10,628 exabytes (EB) of data was determined to be useful if analyzed, while only 5,063 exabytes (EB) of data (47.6%) was analyzed in 2022. How does an open data lakehouse architecture support AI? New insights and relationships are found in this combination.

Data Lake 78

Data Lake Metadata Cost-Benefit Data Warehouse 78

Cloudera

NOVEMBER 6, 2017

Security policies are set once and implemented uniformly upon data and users anywhere. Governance is handled evenly across all environments and applications, both for technical and business metadata. Self-service becomes easy with a unified view of who has rights to what types of data.

Metadata 40

Metadata Data Science Enterprise Risk 40

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. In the past, data movement was defined by ETL: extract, transform, and load.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52