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 80

Data Warehouse Cost-Benefit Metadata Data-driven 80

CIO Business Intelligence

MAY 24, 2022

The company said that IDMC for Financial Services has built-in metadata scanners that can help extract lineage, technical, business, operational, and usage metadata from over 50,000 systems (including data warehouses and data lakes) and applications including business intelligence, data science, CRM, and ERP software.

Finance 135

Finance Management Metadata Data Quality 135

Timo Elliott

JANUARY 6, 2022

The data you’ve collected and saved over the years isn’t free. If storage costs are escalating in a particular area, you may have found a good source of dark data. Analyze your metadata. If you’ve yet to implement data governance, this is another great reason to get moving quickly. Data sense-making.

IT 133

IT Metadata Data-driven Data Governance 133

AWS Big Data

MARCH 7, 2023

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

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 89

Data Lake Data Warehouse Machine Learning Cost-Benefit 89

CIO Business Intelligence

JUNE 23, 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 88

Data Lake Data Warehouse Machine Learning Cost-Benefit 88

Cloudera

MAY 22, 2018

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. save the built model container, along with metadata like who built or deployed it. Cloudera Data Science Workbench 1.4.x

Data Science 41

Data Science Snapshot Machine Learning Metadata 41

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 75

Risk Modeling Management Metadata 75

Analytics Vidhya

SEPTEMBER 13, 2022

This article was published as a part of the Data Science Blogathon. Introduction The purpose of a data warehouse is to combine multiple sources to generate different insights that help companies make better decisions and forecasting. It consists of historical and commutative data from single or multiple sources.

Data Warehouse 348

Data Warehouse Forecasting Big Data Data Science 348

Cloudera

OCTOBER 11, 2022

This leads to the obvious question – how do you do data at scale ? Al needs machine learning (ML), ML needs data science. Data science needs analytics. And they all need lots of data. Different data types need different types of analytics – real-time, streaming, operational, data warehouses.

Snapshot 89

Snapshot Data Science Digital Transformation Metadata 89

Alation

FEBRUARY 13, 2020

Data curation is important in today’s world of data sharing and self-service analytics, but I think it is a frequently misused term. When speaking and consulting, I often hear people refer to data in their data lakes and data warehouses as curated data, believing that it is curated because it is stored as shareable data.

Metadata 52

Metadata Data Warehouse Data Lake Data Governance 52

CIO Business Intelligence

NOVEMBER 22, 2022

External data sharing gets strategic Data sharing between business partners is becoming far easier and much more cooperative, observes Mike Bechtel, chief futurist at business advisory firm Deloitte Consulting. The fabric, especially at the active metadata level, is important, Saibene notes.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

Alation

AUGUST 30, 2022

Centralization of metadata. A decade ago, metadata was everywhere. Consequently, useful metadata was unfindable and unusable. We had data but no data intelligence and, as a result, insights remained hidden or hard to come by. This universe of metadata represents a treasure trove of connected information.

Metadata 52

Metadata Data Quality Data Governance Machine Learning 52

AWS Big Data

SEPTEMBER 25, 2023

With Amazon Redshift , you can run a complex mix of workloads on your data warehouse, such as frequent data loads running alongside business-critical dashboard queries and complex transformation jobs. We also see more and more data science and machine learning (ML) workloads.

Management 91

Management Data Warehouse Metrics Dashboards 91

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

AWS Big Data

MARCH 10, 2023

However, as data processing at scale solutions grow, organizations need to build more and more features on top of their data lakes. Additionally, the task of maintaining and managing files in the data lake can be tedious and sometimes complex. The Iceberg table is synced with the AWS Glue Data Catalog.

Data Lake 98

Data Lake Sales Data Warehouse Snapshot 98

Alation

DECEMBER 15, 2022

June 2017: Dresner Advisory Services names Alation the #1 data catalog in its inaugural Data Catalog End-User Market Study. August 2017: Alation debuts as a leader in the Gartner MQ for Metadata Management Solutions. August 2018: Gartner names Alation a 2X Leader in the MQ for Metadata Management Solutions.

Metadata 52

Metadata Data Governance Data Quality Marketing 52

Alation

FEBRUARY 20, 2020

Prior to the Big Data revolution, companies were inward-looking in terms of data. During this time, data-centric environments like data warehouses dealt only with data created within the enterprise. THE NEED FOR METADATA TOOLS. Given the characteristics of data acquisition, how should it be handled?

IT 52

IT Metadata Data Governance Data Warehouse 52

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

Peter James Thomas

AUGUST 18, 2018

That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. One of the early promises of a Data Lake approach was that – once all relevant data had been ingested – this would be directly leveraged by Data Scientists to derive insight. In Closing.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

Cloudera

FEBRUARY 11, 2019

Cloudera provides a unified platform with multiple data apps and tools, big data management, hybrid cloud deployment flexibility, admin tools for platform provisioning and control, and a shared data experience for centralized security, governance, and metadata management.

Management 42

Management Metadata Analytics Machine Learning 42

Alation

JULY 28, 2022

It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. However, CIOs declare that agility, innovation, security, adopting new capabilities, and time to value — never cost — are the top drivers for cloud data warehousing.

Data Warehouse 69

Data Warehouse Cost-Benefit Data Governance Data-driven 69

Cloudera

MAY 12, 2021

Use cases could include but are not limited to: workload analysis and replication, migrating or bursting to cloud, data warehouse optimization, and more. Data Champions: OVO (PT Visionet Internasional) — Using advanced, intelligent data analytics and machine learning to increase customer conversion rates.

Digital Transformation 65

Digital Transformation Machine Learning Optimization Data Lake 65

Andrew White

OCTOBER 22, 2021

Analytics Tactics (known outcome/known data/BI/analytics v unknown outcome/unknown data/data science/ML) 11. Data Hub Strategy 10. Lakehouse (data warehouse and data lake working together) 8. Data Literacy, training, coordination, collaboration 8. Data Integration tactics 4.

IT 52

IT Data Lake Strategy Data Science 52

Cloudera

MAY 17, 2021

Making a decision on a cloud data warehouse is a big deal. Modernizing your data warehousing experience with the cloud means moving from dedicated, on-premises hardware focused on traditional relational analytics on structured data to a modern platform.

Data Warehouse 87

Data Warehouse Measurement Reporting Testing 87

Domino Data Lab

APRIL 3, 2019

The top three items are essentially “the devil you know” for firms which want to invest in data science: data platform, integration, data prep. Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Rinse, lather, repeat.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

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? How do I meet a growing demand for self-serve BI, while not exploding my data warehouse budgets? How can I optimize my rate of return and continue to drive innovation?

Enterprise 40

Enterprise Data-driven Digital Transformation Machine Learning 40

Octopai

DECEMBER 12, 2021

What enables you to use all those gigabytes and terabytes of data you’ve collected? Metadata is the pertinent, practical details about data assets: what they are, what to use them for, what to use them with. Without metadata, data is just a heap of numbers and letters collecting dust. Where does metadata come from?

Metadata 93

Metadata Management Data Quality Data Governance 93

AWS Big Data

JUNE 6, 2023

Unless, of course, the rest of their data also resides in the Google Cloud. In this post we showcase how we used AWS Glue to move siloed digital analytics data, with inconsistent arrival times, to AWS S3 (our Data Lake) and our central data warehouse (DWH), Snowflake.

Analytics 55

Analytics Data Lake Testing Optimization 55

Cloudera

NOVEMBER 6, 2017

Popular products like Cloudera Data Science Workbench , Analytic DB , Operational DB , and Essentials can be run natively in cloud environments, deploying the cloud service’s resources much the same way they would on-premises. Security policies are set once and implemented uniformly upon data and users anywhere.

Metadata 40

Metadata Data Science Enterprise Risk 40

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

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. 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 Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

IBM Big Data Hub

OCTOBER 4, 2023

A data lakehouse architecture combines the performance of data warehouses with the flexibility of data lakes, to address the challenges of today’s complex data landscape and scale AI. New insights and relationships are found in this combination. All of this supports the use of AI.

Data Lake 83

Data Lake Metadata Cost-Benefit Data Warehouse 83

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. Only metadata will be regenerated. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback. Metadata management .

Metadata 76

Metadata Data Warehouse Snapshot Data Quality 76

Cloudera

AUGUST 30, 2022

Data Lakehouse: Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support artificial intelligence, business intelligence, machine learning, and data engineering use cases on a single platform. Towards Data Science ). Forrester ).

Data Architecture 75

Data Architecture Data Lake Metadata Data Warehouse 75

Sisense

SEPTEMBER 3, 2019

This blog is intended to give an overview of the considerations you’ll want to make as you build your Redshift data warehouse to ensure you are getting the optimal performance. This results in less joins between the metric data in fact tables, and the dimensions. So let’s dive in! OLTP vs OLAP.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78