IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 89

Data Science Data Analytics Prescriptive Analytics Analytics 89

CIO Business Intelligence

JUNE 14, 2023

Candidates are required to complete a minimum of 12 credits, including four required courses: Algorithms for Data Science, Probability and Statistics for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization. Candidates have 90 minutes to complete the exam.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

Sisense

APRIL 17, 2020

We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive. Why use a materialized view?

Data Warehouse 90

Data Warehouse Visualization Statistics Machine Learning 90

AWS Big Data

NOVEMBER 20, 2023

Use case A typical workload for AWS Glue for Apache Spark jobs is to load data from a relational database to a data lake with SQL-based transformations. The following is a visual representation of an example job where the number of workers is 10. On the Graphed metrics tab, configure your preferred statistic, period, and so on.

Metrics 92

Metrics Data Lake Cost-Benefit Dashboards 92

AWS Big Data

APRIL 3, 2024

Manage your Iceberg table with AWS Glue You can use AWS Glue to ingest, catalog, transform, and manage the data on Amazon Simple Storage Service (Amazon S3). With AWS Glue, you can discover and connect to more than 70 diverse data sources and manage your data in a centralized data catalog.

Data Lake 91

Data Lake Snapshot Metadata Data Architecture 91

Sisense

JULY 7, 2020

In-Warehouse Data Prep provides builders with the advanced functionality they need to rapidly transform and optimize raw data creating materialized views on cloud data warehouses. In-Warehouse Data Prep supports both AWS Redshift and Snowflake data warehouses. Additional capabilities.

Slice and Dice 92

Slice and Dice Data Warehouse Analytics Dashboards 92

AWS Big Data

JUNE 6, 2023

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning. This reduces manual data analysis and rule identification efforts from days to hours.

Data Quality 92

Data Quality Statistics Data Lake Visualization 92

Smart Data Collective

NOVEMBER 18, 2020

The Data Scientist profession today is often considered to be one of the most promising and lucrative. The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. Previously, such problems were dealt with by specialists in mathematics and statistics.

Data mining 143

Data mining Data Science Informatics Statistics 143

CIO Business Intelligence

AUGUST 9, 2022

Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved. Data engineer job description.

Analytics 131

Analytics Data Science Statistics Unstructured Data 131

datapine

SEPTEMBER 27, 2022

Business leaders, developers, data heads, and tech enthusiasts – it’s time to make some room on your business intelligence bookshelf because once again, datapine has new books for you to add. We have already given you our top data visualization books , top business intelligence books , and best data analytics books.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. appeared first on IBM Blog.

Machine Learning 64

Machine Learning Data Science Statistics Deep Learning 64

FineReport

MAY 6, 2020

Prinsip database: model data, desain database. Analisis prediktif: analisis axis waktu, principal component analysis, nonparametric regression, statistical process control. Manajemen data: ETL (Extract, Transform, Load), pengelolaan data, management responsibility, metadata. Data Warehouse.

Data mining 98

Data mining Data Warehouse Machine Learning Big Data 98

Sisense

OCTOBER 6, 2020

Let’s consider the differences between the two, and why they’re both important to the success of data-driven organizations. Digging into quantitative data. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?” The challenge comes when the data becomes huge and fast-changing.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

BizAcuity

MAY 16, 2022

He went on to be the head brewer of Guinness and we thank him for not just great hand-crafted beers but subsequent research breakthroughs in statistical research as well. Data allowed Guinness to hold their market dominance for long. Data mining. Data mining allows refining and analyzing of the data on a near-real time basis.

Business Intelligence 52

Business Intelligence Data mining Visualization Data Lake 52

FineReport

JULY 28, 2021

Definition: BI vs Data Science vs Data Analytics. Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information.

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

AWS Big Data

MAY 2, 2023

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to analyze all your data efficiently and securely. Users such as data analysts, database developers, and data scientists use SQL to analyze their data in Amazon Redshift data warehouses.

Data Warehouse 60

Data Warehouse Software Visualization IoT 60

FineReport

FEBRUARY 28, 2020

Enterprise reporting is a process of extracting, processing, organizing, analyzing, and displaying data in the companies. It uses enterprise reporting tools to organize data into charts, tables, widgets, or other visualizations. The central one is the data visualization technology at the display level.

Reporting 96

Reporting Enterprise Visualization Business Intelligence 96

Birst BI

NOVEMBER 20, 2018

Business Intelligence tools support this iterative process: data may be available or made available in a data warehouse, and its Analytics tools such as charts, reports, and dashboards provide the visual support for the business discussions. Finally, Business Intelligence tools visualize the results to end-users.

Statistics 84

Statistics Machine Learning Deep Learning Optimization 84

Sisense

MAY 27, 2019

They take the raw data and polish it into valuable insights that fit your business and meet your specific needs. The data scientist is mostly focused on the relationships of the data within an organization’s database. They use their skills to compare data, and perform statistical analyses on the data, which provide insights.

Statistics 75

Statistics Machine Learning Visualization Big Data 75

Cloudera

SEPTEMBER 29, 2022

The data product is not just the data itself, but a bunch of metadata that surrounds it — the simple stuff like schema is a given. In the data fabric implementation, the concepts in data mesh map to real-world artifacts in a data architecture. Figure 2.

Data Architecture 73

Data Architecture Metadata Data Warehouse Sales 73

Darkhorse

DECEMBER 11, 2017

Data Steward – this skillset is alive and well in most organizations. Almost everyone has a data warehouse, talks about the ETL process, and has had discussions around the business rules of cleaning up and storing their data. What they should be talking about is how to get the data out more quickly and cleanly.

Analytics 80

Analytics Visualization Data Warehouse Data-driven 80

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. Business/Data Analyst: The business analyst is all about the “meat and potatoes” of the business.

Data Quality 241

Data Quality Metrics Data-driven Management 241

Cloudera

APRIL 9, 2021

Enterprise data warehouse platform owners face a number of common challenges. In this article, we look at seven challenges, explore the impacts to platform and business owners and highlight how a modern data warehouse can address them. ETL jobs and staging of data often often require large amounts of resources.

Data Warehouse 58

Data Warehouse Cost-Benefit Statistics Data-driven 58

Sisense

JUNE 30, 2020

In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways. One of the downsides of the role that data now plays in the modern business world is that users can be overloaded with jargon and tech-speak, which can be overwhelming.

Visualization 93

Visualization Analytics Dashboards Data-driven 93

Smarten

NOVEMBER 22, 2017

Fortunately, today’s new self-serve business intelligence solutions allow for ease-of-use, bringing together these varied techniques in a simple interface with tools that allow business users to utilize advanced analytics without the skill or knowledge of a data scientist, analyst or IT team member.

Analytics 40

Analytics IT Business Intelligence Visualization 40

datapine

MAY 29, 2019

BI architecture has emerged to meet those requirements, with data warehousing as the backbone of these processes. One of the BI architecture components is data warehousing. Each of that component has its own purpose that we will discuss in more detail while concentrating on data warehousing. Data integration.

Business Intelligence 196

Business Intelligence Data Warehouse Dashboards Visualization 196

Birst BI

OCTOBER 30, 2018

To generate accurate probabilities of future behavior, predictive analytics combine historical data from any number of applications with statistical algorithms. This may involve integrating different technologies, like cloud sources, on-premise databases, data warehouses and even spreadsheets.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

CIO Business Intelligence

OCTOBER 7, 2022

BI analysts, with an average salary of $71,493 according to PayScale , provide application analysis and data modeling design for centralized data warehouses and extract data from databases and data warehouses for reporting, among other tasks. BI encompasses numerous roles. Organization: Microsoft.

Business Intelligence 145

Business Intelligence Data Warehouse Visualization Dashboards 145

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 92

Analytics Data Warehouse Testing Dashboards 92

AWS Big Data

NOVEMBER 29, 2023

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

Data Warehouse 97

Data Warehouse Data Lake Analytics Machine Learning 97

Cloudera

NOVEMBER 25, 2020

How can he make it easy to see statistics, and do calculations, on discovered commonalities, across structured and unstructured data? How can users drill down, in non-technical ways, to quickly interact with data that explains what correlations seem to matter? The Vision of a Discovery Data Warehouse.

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57

Occam's Razor

APRIL 4, 2011

Diana has loads of observations, supported by visuals (sometimes it really helps to show the search results or the emails or the Facebook ad) with highlights (actually lowlights) in red, and finally recommendations. Also mere existence of statistics is not sufficient. Or, in quite concise ways? Only one of those two work.

Reporting 127

Reporting Metrics Dashboards Statistics 127

CIO Business Intelligence

JANUARY 20, 2023

But the benefits of BI extend beyond business decision-making, according to data visualization vendor Tableau , including the following: Data-driven business decisions: The ability to drive business decisions with data is the central benefit of BI.

Business Intelligence 144

Business Intelligence Dashboards Data mining OLAP 144

FineReport

APRIL 11, 2023

Every user can now create interactive reports and utilize data visualization to disseminate knowledge to both internal and external stakeholders. A business intelligence dashboard, also known as a BI dashboard, is a tool that presents important business metrics and data points in a visual and analytical format on a single screen.

Dashboards 40

Dashboards Business Intelligence Cost-Benefit Metrics 40

Smart Data Collective

OCTOBER 17, 2022

A point of data entry in a given pipeline. Examples of an origin include storage systems like data lakes, data warehouses and data sources that include IoT devices, transaction processing applications, APIs or social media. The final point to which the data has to be eventually transferred is a destination.

Data Warehouse 130

Data Warehouse Data Lake Visualization Big Data 130

Sisense

DECEMBER 3, 2019

The rapid growth of data volumes has effectively outstripped our ability to process and analyze it. The first wave of digital transformations saw a dramatic decrease in data storage costs. On-demand compute resources and MPP cloud data warehouses emerged. Optimize raw data using materialized views.

Data Warehouse 90

Data Warehouse Analytics Digital Transformation Forecasting 90

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. Communicate and visualize the results. A data catalog can assist directly with every step, but model development.

Metadata 59

Metadata Data Quality Statistics Data Science 59