IBM Big Data Hub

AUGUST 5, 2019

In a previous blog , I explained how data science capabilities, massive parallel processing (MPP). and usability improvements in data warehouse appliances can help the bottom line—and why old-fashioned architectures might not cut it. But what does that look like in practice?

Data Warehouse 62

Data Warehouse Data Science IT 62

Cloudera

FEBRUARY 9, 2021

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

Data Warehouse 69

Data Warehouse Cost-Benefit Metadata Management 69

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

Machine Learning 58

Machine Learning Data Science Statistics Deep Learning 58

Analytics Vidhya

FEBRUARY 6, 2023

Introduction The demand for data to feed machine learning models, data science research, and time-sensitive insights is higher than ever thus, processing the data becomes complex. To make these processes efficient, data pipelines are necessary.

Machine Learning 242

Machine Learning Data Science Modeling Analytics 242

Data Virtualization

JULY 1, 2021

If we look at a typical , many of its stages have more to do with data than science. Before data scientists can begin their work regarding data science, they often must begin by: Finding the right data Gaining access.

Data Science 52

Data Science Data Lake IT Data Warehouse 52

DataKitchen

DECEMBER 7, 2021

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. In the book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today’s organizations.

Data-driven 246

Data-driven Data Governance Big Data Data Science 246

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

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. This blog post provides a concise session summary, a video, and a written transcript. data science’s emergence as an interdisciplinary field – from industry, not academia. Session Summary. Key highlights from the session include.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Cloudera

SEPTEMBER 17, 2018

These trends and demands lead to stress for existing data warehouse solutions – scale, efficiency, security integrations, IT budgets, ease of access. Cloudera recently launched Cloudera Data Warehouse, a modern data warehousing solution.

Data Warehouse 53

Data Warehouse Optimization Visualization Unstructured Data 53

Cloudera

OCTOBER 4, 2023

A unique architecture to optimize for real-time data warehousing and business analytics: Cloudera Data Platform (CDP) offers Apache Kudu as part of our Data Hub cloud service, providing a consistent, dependable way to support the ingestion of data streams into our analytics environment, in real time, and at any scale.

Data Warehouse 86

Data Warehouse Experimentation Cost-Benefit Dashboards 86

DataKitchen

AUGUST 19, 2021

Each data source is updated on its own schedule, for example, daily, weekly or monthly. The DataKitchen Platform ingests data into a data lake and runs Recipes to create a data warehouse leveraged by users and self-service data analysts. The third set of domains are cached data sets (e.g., Conclusion.

Data Warehouse 246

Data Warehouse Data Lake Manufacturing Testing 246

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

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. DataOps is a hot topic in 2021.

Testing 307

Testing Machine Learning Consulting Data Quality 307

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 91

Data Lake Data Warehouse Machine Learning Cost-Benefit 91

Data Virtualization

FEBRUARY 22, 2023

Reading Time: 2 minutes The data lakehouse attempts to combine the best parts of the data warehouse with the best parts of data lakes while avoiding all of the problems inherent in both. However, the data lakehouse is not the last word in data.

Data Lake 52

Data Lake Data Warehouse Data Integration Management 52

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 The post Now Available: Cloudera Data Science Workbench Release 1.4 or higher 5.x

Data Science 41

Data Science Snapshot Machine Learning Metadata 41

Data Virtualization

FEBRUARY 22, 2023

Reading Time: 2 minutes The data lakehouse attempts to combine the best parts of the data warehouse with the best parts of data lakes while avoiding all of the problems inherent in both. However, the data lakehouse is not the last word in data.

Data Lake 52

Data Lake Data Warehouse Data Integration Management 52

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 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

Data Virtualization

JANUARY 19, 2023

Reading Time: 2 minutes Today, many businesses are modernizing their on-premises data warehouses or cloud-based data lakes using Microsoft Azure Synapse Analytics. Unfortunately, with data spread.

Data Analytics 52

Data Analytics Data Lake Data Warehouse Analytics 52

CIO Business Intelligence

APRIL 25, 2022

Now generally available, the M&E data lakehouse comes with industry use-case specific features that the company calls accelerators, including real-time personalization, said Steve Sobel, the company’s global head of communications, in a blog post. Features focus on media and entertainment firms.

Recreation/Entertainment 117

Recreation/Entertainment Data Lake Data Warehouse Unstructured Data 117

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 91

Snapshot Data Science Digital Transformation Metadata 91

Sisense

DECEMBER 3, 2019

Data is the New Oil” was coined by The Economist in May 2017 and became a mantra for organizations to drive new wealth from data. But in reality, data by itself has no value. The rapid growth of data volumes has effectively outstripped our ability to process and analyze it. Optimize raw data using materialized views.

Data Warehouse 90

Data Warehouse Analytics Digital Transformation Forecasting 90

Cloudera

AUGUST 8, 2022

The general availability covers Iceberg running within some of the key data services in CDP, including Cloudera Data Warehouse ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). Cloudera Data Engineering (Spark 3) with Airflow enabled. Cloudera Machine Learning .

Snapshot 90

Snapshot Data Warehouse Machine Learning Cost-Benefit 90

Data Virtualization

APRIL 7, 2022

As far back as 2011 Gartner proposed the concept of a logical data warehouse as a way to overcome some of the challenges organizations. The post Does Data Always Need to End Up in a Centralized Repository? Reading Time: 3 minutes This is an age-old question, and one that has been asked many times over the years.

Data Warehouse 52

Data Warehouse Data Integration Management Data Lake 52

Timo Elliott

JANUARY 6, 2022

Like the proverbial man looking for his keys under the streetlight , when it comes to enterprise data, if you only look at where the light is already shining, you can end up missing a lot. Modern technologies allow the creation of data orchestration pipelines that help pool and aggregate dark data silos. Data sense-making.

IT 133

IT Metadata Data-driven Data Governance 133

datapine

AUGUST 29, 2022

Best for : the new intern who has no idea what data science even means. An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

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 80

Forecasting Data Warehouse Machine Learning Modeling 80

DataRobot Blog

MARCH 8, 2023

Every modern enterprise has a unique set of business data collected as part of their sales, operations, and management processes. Using DataRobot, companies can monitor their models in production for accuracy and data drift, in addition to retraining them proactively.

Enterprise 105

Enterprise Experimentation Machine Learning Modeling 105

CDW Research Hub

JULY 27, 2021

Data volumes are growing exponentially, and traditional, on-premises data warehouses are constrained, overly complex, and costly to scale. In this way, the Cloud Data Warehouse Accelerator enables a seamless transition to Snowflake. Reduce the total cost of ownership of the data infrastructure.

Data Warehouse 52

Data Warehouse Data Lake Data Architecture Consulting 52

Sisense

AUGUST 16, 2021

New software companies are born, live, and grow on the cloud, their cloud data teams never handling a single server. Every other type of company is quickly transforming into a data company, whether it knows it or not, and the rapidly growing volumes of information organizations deal with have to get stored someplace. .”

Data Warehouse 52

Data Warehouse Analytics Cost-Benefit Visualization 52

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 56

Big Data Data Warehouse Machine Learning Business Intelligence 56

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

JUNE 2, 2022

allowing developers to connect to any data source anywhere with any structure, process it, and deliver to any destination. This blog aims to answer two questions: What is a universal data distribution service? Why does every organization need it when using a modern data stack? What is the modern data stack?

Enterprise 101

Enterprise Data Lake Data Collection Data-driven 101

CDW Research Hub

DECEMBER 4, 2019

There are many benefits of using a cloud-based data warehouse, and the market for cloud-based data warehouses is growing as organizations realize the value of making the switch from an on-premises data warehouse.

Data Warehouse 52

Data Warehouse Data mining Data Lake Dashboards 52

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

NOVEMBER 3, 2023

To add to the complexity, there is a shortage of finding the right people with the right skills to take on development or data science projects. When you make it easier to work with events, other users like analysts and data engineers can start gaining real-time insights and work with datasets when it matters most.

Cost-Benefit 78

Cost-Benefit Experimentation Testing Digital Transformation 78

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 76

Risk Modeling Management Metadata 76