Cloudera

OCTOBER 4, 2023

Different data streams will have different characteristics, and having a platform flexible enough to adapt, with things like flexible partitioning for example, will be essential in adapting to different source volume characteristics. Kudu has this covered. The post Don’t Blink: You’ll Miss Something Amazing!

Data Warehouse 84

Data Warehouse Experimentation Cost-Benefit Dashboards 84

Boris Evelson

JUNE 14, 2023

No matter what technology foundation you’re using – a data lake, a data warehouse, data fabric, data mesh, etc. – BI applications are where business users consume data and turn it into actionable insights and decisions. The BI market has […]

Business Intelligence 98

Business Intelligence Data Lake Data Warehouse Data-driven 98

IBM Big Data Hub

OCTOBER 30, 2023

Thus, DB2 PureScale on AWS equips this insurance company to innovate and make data-driven decisions rapidly, maintaining a competitive edge in a saturated market. The platform provides an intelligent, self-service data ecosystem that enhances data governance, quality and usability.

Insurance 84

Insurance Data Warehouse Data-driven Unstructured Data 84

AWS Big Data

OCTOBER 18, 2023

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. About the Authors Tahir Aziz is an Analytics Solution Architect at AWS.

Snapshot 91

Snapshot Management Data Warehouse Dashboards 91

CIO Business Intelligence

NOVEMBER 14, 2022

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.

Data mining 144

Data mining Data-driven Statistics OLAP 144

CIO Business Intelligence

FEBRUARY 16, 2023

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Digital Transformation 105

datapine

MAY 9, 2019

Business intelligence concepts refer to the usage of digital computing technologies in the form of data warehouses, analytics and visualization with the aim of identifying and analyzing essential business-based data to generate new, actionable corporate insights. The data warehouse. 1) The raw data.

Business Intelligence 270

Business Intelligence Dashboards Data Warehouse Sales 270

IBM Big Data Hub

SEPTEMBER 19, 2023

Having the right data strategy and data architecture is especially important for an organization that plans to use automation and AI for its data analytics. The types of data analytics Predictive analytics: Predictive analytics helps to identify trends, correlations and causation within one or more datasets.

Data Science 83

Data Science Data Analytics Prescriptive Analytics Analytics 83

CIO Business Intelligence

DECEMBER 1, 2022

Now halfway into its five-year digital transformation, PepsiCo has checked off many important boxes — including employee buy-in, Kanioura says, “because one way or another every associate in every plant, data center, data warehouse, and store are using a derivative of this transformation.”

Digital Transformation 144

Digital Transformation IoT Data-driven KPI 144

Sisense

MARCH 27, 2019

To provide real-time data, these platforms use smart data storage solutions such as Redshift data warehouses , visualizations, and ad hoc analytics tools. This allows dashboards to show both real-time and historic data in a holistic way. Why is Real-Time BI Crucial for Organizations?

Business Intelligence 74

Business Intelligence Data Warehouse Predictive Modeling Visualization 74

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.

Enterprise 105

Enterprise Experimentation Machine Learning Modeling 105

datapine

MARCH 31, 2022

This could involve anything from learning SQL to buying some textbooks on data warehouses. It allows its users to extract actionable insights from their data in real-time with the help of predictive analytics and artificial intelligence technologies. Business Intelligence Job Roles.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Smart Data Collective

NOVEMBER 18, 2020

Every Data Scientist needs to know Data Mining as well, but about this moment we will talk a bit later. Where to Use Data Science? Where to Use Data Mining? Data Mining is an important research process. Practical experience.

Data mining 143

Data mining Data Science Informatics Statistics 143

BizAcuity

MAY 16, 2022

Whereas, integrating data sources can provide you with a picture of where your customer is coming from, how long they spend on your website, what can be improved in the entire buying process among others. Integrating data allows you to perform cross-database queries, which like portals provide you with endless possibilities.

Business Intelligence 52

Business Intelligence Data mining Visualization Data Lake 52

Smart Data Collective

MARCH 21, 2022

More case studies are added every day and give a clear hint – data analytics are all set to change, again! . Data Management before the ‘Mesh’. In the early days, organizations used a central data warehouse to drive their data analytics. The cloud age did address that issue to a certain extent.

Management 66

Management Data Lake Uncertainty Data Warehouse 66

Jedox

OCTOBER 22, 2020

In addition to using data to inform your future decisions, you can also use current data to make immediate decisions. Some of the technologies that make modern data analytics so much more powerful than they used t be include data management, data mining, predictive analytics, machine learning and artificial intelligence.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

CIO Business Intelligence

JUNE 14, 2023

CDP Data Analyst The Cloudera Data Platform (CDP) Data Analyst certification verifies the Cloudera skills and knowledge required for data analysts using CDP. The exam requires the candidate to use applications involving natural language processing, speech, computer vision, and predictive analytics.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

CIO Business Intelligence

APRIL 26, 2022

Google Cloud Platform fuels Backcountry’s stack, with Google’s Looker providing real-time analytics and predictive analytics and Google’s BigQuery data warehouse handling “everything end to end: product, sales, order, inventory, supply chain, and more.”

Data Lake 98

Data Lake Dashboards Recreation/Entertainment Sales 98

In(tegrate) the Clouds

AUGUST 4, 2016

The second trend is the data lake and how to complement, extend — and in some cases replace — the traditional data warehouse with a reference architecture that is built to handle all new and future sources and enable more proactive and predictive analytics.

IoT 45

IoT Internet of Things Data Lake Data Warehouse 45

Sisense

MAY 31, 2020

Every customer has something to teach us about how companies use data to transform a business or change lives. “We knew our journey with predictive analytics and sentiment analysis was going to be a gradual progression that would eventually help us understand and better serve our customers.

Marketing 98

Marketing Reporting Data Warehouse Predictive Analytics 98

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.

Metadata 52

Metadata IT Data Governance Data Warehouse 52

CIO Business Intelligence

FEBRUARY 14, 2023

This has also accelerated the execution of edge computing solutions so compute and real-time decisioning can be closer to where the data is generated. AI continues to transform customer engagements and interactions with chatbots that use predictive analytics for real-time conversations.

Data Strategy 116

Data Strategy Strategy Machine Learning Data-driven 116

IBM Big Data Hub

JULY 6, 2023

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues.

Machine Learning 58

Machine Learning Data Science Statistics Deep Learning 58

AWS Big Data

MAY 16, 2024

As an AWS Partner, CARTO offers a software solution on the curated digital catalog AWS Marketplace that seamlessly integrates distinctive capabilities for spatial visualization, analysis, and app development directly within the AWS data warehouse environment. To learn more, visit CARTO.

Data Warehouse 107

Data Warehouse Visualization Cost-Benefit Optimization 107

Sisense

OCTOBER 6, 2020

Traditional methods of gathering and organizing data can’t organize, filter, and analyze this kind of data effectively. What seem at first to be very random, disparate forms of qualitative data require the capacity of data warehouses , data lakes , and NoSQL databases to store and manage them.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

AWS Big Data

OCTOBER 18, 2023

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that provides the flexibility to use provisioned or serverless compute for your analytical workloads. You can get faster insights without spending valuable time managing your data warehouse. Fault tolerance is built in.

Analytics 102

Analytics Data Warehouse Testing Dashboards 102

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. Read how to get nominated. link] #DataImpactAwards.

Machine Learning 41

Machine Learning Predictive Analytics Data-driven Data Science 41

AWS Big Data

MARCH 27, 2024

Achieving this will also improve general public health through better and more timely interventions, identify health risks through predictive analytics, and accelerate the research and development process. You can send data from your streaming source to this resource for ingesting the data into a Redshift data warehouse.

Data Analytics 99

Data Analytics Analytics Data Warehouse Data Lake 99

Cloudera

SEPTEMBER 12, 2018

Voya Financial prevented millions of dollars of fraudulent transactions by deploying predictive analytic capabilities on Cloudera. Modern Data Warehousing: Barclays (nominated together with BlueData ). Barclays’ data warehouse ingests data from more than 300 sources and runs thousands of jobs daily.

Machine Learning 44

Machine Learning Big Data Data Science Data Warehouse 44

Cloudera

MARCH 2, 2018

Applying data analytics and machine learning to large raw datasets will likely also yield us new and unexpected insights as these techniques and tools allow us to unearth patterns and seek potential explanations for those in contrast to responding to a predefined set of questions.

Data-driven 42

Data-driven Prescriptive Analytics Descriptive Analytics Machine Learning 42

CIO Business Intelligence

AUGUST 18, 2022

Traditionally, organizations have maintained two systems as part of their data strategies: a system of record on which to run their business and a system of insight such as a data warehouse from which to gather business intelligence (BI). You can intuitively query the data from the data lake.

Data Lake 140

Data Lake Data Warehouse Unstructured Data Business Intelligence 140

AWS Big Data

NOVEMBER 8, 2023

Because Gilead is expanding into biologics and large molecule therapies, and has an ambitious goal of launching 10 innovative therapies by 2030, there is heavy emphasis on using data with AI and machine learning (ML) to accelerate the drug discovery pipeline. Loading data is a key process for any analytical system, including Amazon Redshift.

Data Lake 81

Data Lake Data Warehouse Cost-Benefit Optimization 81

AWS Big Data

JULY 12, 2023

Introduction to Amazon Redshift Amazon Redshift is a fast, fully-managed, self-learning, self-tuning, petabyte-scale, ANSI-SQL compatible, and secure cloud data warehouse. Thousands of customers use Amazon Redshift to analyze exabytes of data and run complex analytical queries.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52

Cloudera

FEBRUARY 8, 2021

Predictive Analytics – predictive analytics based upon AI and machine learning (predictive maintenance, demand-based inventory optimization as examples). Security & Governance – an integrated set of security, management and governance technologies across the entire data lifecycle.

Manufacturing 114

Manufacturing Data Warehouse Sales Predictive Analytics 114

Jet Global

JULY 11, 2019

With major advances being made in artificial intelligence and machine learning, businesses are investing heavily in advanced analytics to get ahead of the competition and increase their bottom line. Demand forecasting is an area of predictive analytics best known for understanding consumer demand for goods and services.

Business Intelligence 72

Business Intelligence Forecasting Data Warehouse Predictive Analytics 72

FineReport

JUNE 24, 2021

Technicals such as data warehouse, online analytical processing (OLAP) tools, and data mining are often binding. On the opposite, it is more of a comprehensive application of data warehouse, OLAP, data mining, and so forth. Predictive analytics and modeling.

Business Intelligence 84

Business Intelligence OLAP Data mining Visualization 84

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 63

Slice and Dice Management Data Warehouse Analytics 63