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. Cloudera Data Science Workbench 1.4.x for the Oracle Big Data Appliance). appeared first on Cloudera Blog.

Data Science 41

Data Science Snapshot Machine Learning Metadata 41

CDW Research Hub

JULY 18, 2022

While many organizations understand the business need for a data and analytics cloud platform , few can quickly modernize their legacy data warehouse due to a lack of skills, resources, and data literacy. Overall data architecture and strategy. Cost reduction and best business practices.

Optimization 87

Optimization Data Lake Data Warehouse Manufacturing 87

Jen Stirrup

MAY 3, 2020

Azure Synapse is an Azure-based cloud analytics service that provides insights on all data across data warehouses and big data analytics systems. It combines familiar SQL technologies such as data warehousing with Spark technologies used in big data analytics. Azure Synapse docs on GitHub.

Big Data 57

Big Data Data Warehouse Machine Learning Business Intelligence 57

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 81

Data Warehouse Cost-Benefit Metadata Data-driven 81

AWS Big Data

AUGUST 7, 2023

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Data scientists use them to identify forecasting data characteristics. and Karra Taniskidou, E.

Forecasting 86

Forecasting Data Warehouse Machine Learning Modeling 86

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.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

IBM Big Data Hub

NOVEMBER 3, 2023

However, not all of it is necessarily actionable and some get stuck in queues or big data batch processing. Additionally, Apache Flink contextualizes your data by detecting patterns, enabling you to understand how things happen alongside each other.

Cost-Benefit 75

Cost-Benefit Experimentation Testing Digital Transformation 75

Data Virtualization

DECEMBER 15, 2022

Reading Time: 3 minutes One of the biggest challenges for organizations is to integrate data from various sources. Despite modern advancements such as big data technologies and cloud, data often ends up in organized silos, but this means that cloud data is separated from.

Big Data 52

Big Data Data Integration Technology Management 52

datapine

MARCH 31, 2022

This could involve anything from learning SQL to buying some textbooks on data warehouses. BI developer skills encompass crafting and executing data-driven queries upon request as well as the ongoing technical development of a company’s BI platforms or solutions. Business Intelligence Job Roles. Yes, they exist.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Insight

AUGUST 20, 2019

If you want to get started with Spark, check out this blog on how to setup your very own Spark cluster on AWS here. Flink An alternative to Spark, Flink has gotten a lot of traction in the Data Engineering community. Our Fellows have used it in their projects, often in conjunction with Spark, for the exploration of Reddit data.

Data Warehouse 71

Data Warehouse Big Data Data-driven Data Processing 71

CDW Research Hub

APRIL 20, 2020

Many organizations move from a traditional data warehouse to a hybrid or cloud-based data warehouse to help alleviate their struggles with rapidly expanding data, new users and use cases, and a growing number of diverse tools and applications.

Data Warehouse 52

Data Warehouse Consulting Data-driven Structured Data 52

IBM Big Data Hub

OCTOBER 16, 2023

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. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

Risk 73

Risk Modeling Management Metadata 73

Cloudera

OCTOBER 23, 2017

Your enterprise big data and machine learning initiatives can be delayed, fail, or risk security breach unless you choose the most mature, most tightly integrated platform. One primary goal was making multi-disciplinary analytics workloads run smoothly and efficiently in the cloud when backed by our new Shared Data Experience.

Machine Learning 40

Machine Learning Big Data Risk Data Science 40

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. Cloudera’s Positioning. Cloudera is positioned furthest for “Completeness of Vision.”

Management 43

Management Metadata Analytics Machine Learning 43

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. Subscribe to Alation's Blog.

Metadata 52

Metadata IT Data Governance Data Warehouse 52

IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

datapine

AUGUST 29, 2022

The saying “knowledge is power” has never been more relevant, thanks to the widespread commercial use of big data and data analytics. The rate at which data is generated has increased exponentially in recent years. Essential Big Data And Data Analytics Insights. million searches per day and 1.2

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Cloudera

SEPTEMBER 12, 2018

It was deeply gratifying to see so many organizations deploying the tools and techniques of data science and advanced analytics to solve difficult and important problems. I predict that next year’s competition will be even more amazing as we continue pushing the frontiers of data science forward. Technical Impact.

Machine Learning 45

Machine Learning Big Data Data Science Data Warehouse 45

IBM Big Data Hub

NOVEMBER 8, 2023

We are expanding IBM Db2 Warehouse on Power with a new Base Rack Express at a 30% lower entry list price, adding to today’s S, M and L configurations, while still providing the same total-solution experience, including Db2 Data Warehouse’s connectivity with watsonx.data to unlock the potential of data for analytics and AI.

Strategy 96

Strategy Cost-Benefit Data-driven Digital Transformation 96

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

DataKitchen

DECEMBER 7, 2021

Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI, by Randy Bean. This book is not available until January 2022, but considering all the hype around the data mesh, we expect it to be a best seller. A distributed data mesh is a better choice. How did we get here?

Data-driven 246

Data-driven Data Governance Big Data Data Science 246

Birst BI

NOVEMBER 20, 2018

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. To solve practical decision problems, the Data Scientist typically uses combinations of these methods.

Statistics 84

Statistics Machine Learning Deep Learning Optimization 84

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 56

Analytics Data Lake Testing Optimization 56

Data Virtualization

FEBRUARY 24, 2022

We say that data storage, like matter, adheres to the Law of Increments, which states that, according. The post Introducing the Liquid Data Model appeared first on Data Virtualization blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Modeling 59

Modeling Data Integration Software Management 59

Data Virtualization

OCTOBER 5, 2022

For this reason, organizations must periodically revisit their data architectures, to ensure that they are aligned with current business goals.

Data Lake 52

Data Lake Data Architecture Data Integration Management 52

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

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Genie — Distributed big data orchestration service by Netflix.

Testing 300

Testing Machine Learning Consulting Data Quality 300

Cloudera

MAY 10, 2018

Click to tweet : Nominations are now open for the sixth annual Cloudera Data Impact Awards! With advancements in exploratory data science, machine learning, predictive analytics, AI, and data engineering, the world is increasingly driven by data. Learn more about the Data Impact Awards.

Machine Learning 41

Machine Learning Predictive Analytics Data-driven Data Science 41

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. There are two common ways to approach cloud services with Cloudera.

Metadata 40

Metadata Data Science Enterprise Risk 40

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. How does an open data lakehouse architecture support AI? All of this supports the use of AI.

Data Lake 81

Data Lake Metadata Cost-Benefit Data Warehouse 81

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

Cloudera

JUNE 29, 2018

Over the past decade, Cloudera University has taught more than 50,000 developers, administrators, analysts, and data scientists how to apply big data technologies. Blended learning sessions for Cloudera Administrator Training and Data Analyst Training begin later this summer and early fall.

Interactive 43

Interactive Big Data Technology Data Warehouse 43

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

Cloudera

AUGUST 31, 2020

Given the prohibitive cost of scaling it, in addition to the new business focus on data science and the need to leverage public cloud services to support future growth and capability roadmap, SMG decided to migrate from the legacy data warehouse to Cloudera’s solution using Hive LLAP. The case for a new Data Warehouse?

Slice and Dice 62

Slice and Dice Management Data Warehouse Analytics 62

Cloudera

JULY 9, 2018

Some call it the “golden triangle,” but in this blog, we refer to it as the iron triangle. Most organizations struggle to unlock data science in the enterprise. It’s powerful features finally get data scientists, analysts, and business teams speaking the same language. Why the Data Science Iron Triangle Matters.

Machine Learning 42

Machine Learning Data Science Testing Visualization 42

Sisense

FEBRUARY 24, 2021

Whatever a company does, how it uses data is a key differentiator in its success or failure. Whether that data is generated internally or gathered from an external application used by customers, organizations now use on-demand cloud computing resources to make sense of the data, discover trends, and make intelligent forecasts.

Data Warehouse 69

Data Warehouse Big Data Metrics Visualization 69

AWS Big Data

MARCH 28, 2023

In a data warehouse, a dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Run the AWS Glue job Confirm if you see the employee dataset in the path s3://scd-blog-landing/dataset/employee/. Vijay Velpula is a Data Architect with AWS Professional Services.

Data Lake 73

Data Lake Testing Snapshot Sales 73