Smart Data Collective

OCTOBER 27, 2021

An interactive analytics application gives users the ability to run complex queries across complex data landscapes in real-time: thus, the basis of its appeal. Interactive analytics applications present vast volumes of unstructured data at scale to provide instant insights. Amazon Redshift is a fast and widely used data warehouse.

Analytics 130

Analytics Analytics Data Warehouse Business Intelligence 130

Dataconomy

MAY 10, 2023

Open source business intelligence software is a game-changer in the world of data analysis and decision-making. It has revolutionized the way businesses approach data analytics by providing cost-effective and customizable solutions that are tailored to specific business needs.

Business Intelligence 178

Business Intelligence Business Intelligence Power BI Data Analysis 178

Flipboard

APRIL 17, 2023

Enterprises often rely on data warehouses and data lakes to handle big data for various purposes, from business intelligence to data science. A new approach, called a data lakehouse, aims to …

Data Lakes 123

Data Lakes Data Warehouse Big Data Big Data 123

IBM Journey to AI blog

FEBRUARY 2, 2023

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

Data Warehouse 58

Data Warehouse Cloud Data Analytics Analytics 58

Women in Big Data

NOVEMBER 27, 2024

A data warehouse is a centralized repository designed to store and manage vast amounts of structured and semi-structured data from multiple sources, facilitating efficient reporting and analysis. Begin by determining your data volume, variety, and the performance expectations for querying and reporting.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Data Science Dojo

FEBRUARY 1, 2023

Azure Synapse provides a unified platform to ingest, explore, prepare, transform, manage, and serve data for BI (Business Intelligence) and machine learning needs. DWUs (Data Warehouse Units) can customize resources and optimize performance and costs.

Azure 195

Azure SQL Analytics Analytics 195

How to Learn Machine Learning

APRIL 26, 2025

Tools like Python (with pandas and NumPy), R, and ETL platforms like Apache NiFi or Talend are used for data preparation before analysis. Data Analysis and Modeling This stage is focused on discovering patterns, trends, and insights through statistical methods, machine-learning models, and algorithms.

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

Pickl AI

JANUARY 15, 2025

Summary: Business Intelligence tools are software applications that help organizations collect, process, analyse, and visualize data from various sources. Introduction Business Intelligence (BI) tools are essential for organizations looking to harness data effectively and make informed decisions.

Business Intelligence 52

Business Intelligence Business Intelligence Power BI Data Visualization 52

Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Pickl AI

JANUARY 28, 2025

Summary: Understanding Business Intelligence Architecture is essential for organizations seeking to harness data effectively. This framework includes components like data sources, integration, storage, analysis, visualization, and information delivery. What is Business Intelligence Architecture?

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Tableau

JUNE 8, 2021

We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models. Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions.

Tableau 102

Tableau Data Lakes Data Warehouse SQL 102

AWS Machine Learning Blog

APRIL 3, 2025

Part of a comprehensive approach to using artificial intelligence and machine learning (AI/ML) and generative AI includes a strong data strategy that can help provide high quality and reliable data. When needed, the system can access an ODAP data warehouse to retrieve additional information.

AWS 81

AWS Data Governance Data Silos SQL 81

Dataconomy

MARCH 4, 2025

Data mining is a fascinating field that blends statistical techniques, machine learning, and database systems to reveal insights hidden within vast amounts of data. Businesses across various sectors are leveraging data mining to gain a competitive edge, improve decision-making, and optimize operations.

Data Mining 91

Data Mining Data Mining Data Mining Decision Trees 91

Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements.

ETL 126

ETL Hadoop Data Warehouse Data Pipeline 126

AWS Machine Learning Blog

DECEMBER 29, 2022

You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. Users can also interact with data with ODBC, JDBC, or the Amazon Redshift Data API. arrange(card_brand).

AWS 135

AWS Machine Learning Machine Learning Database 135

Dataconomy

SEPTEMBER 4, 2023

Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence. Ensure that data is clean, consistent, and up-to-date.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

phData

NOVEMBER 9, 2023

This data mesh strategy combined with the end consumers of your data cloud enables your business to scale effectively, securely, and reliably without sacrificing speed-to-market. What is a Cloud Data Warehouse? For example, most data warehouse workloads peak during certain times, say during business hours.

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

IBM Journey to AI blog

APRIL 25, 2023

In a prior blog , we pointed out that warehouses, known for high-performance data processing for business intelligence, can quickly become expensive for new data and evolving workloads. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

Data Warehouse 58

Data Warehouse Data Engineering Data Engineering Data Engineer 58

ODSC - Open Data Science

FEBRUARY 24, 2023

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. You can even connect directly to 20+ data sources to work with data within minutes.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

IBM Journey to AI blog

MAY 9, 2023

Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines across IT environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1]

Data Warehouse 92

Data Warehouse AWS AI AI 92

IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

DataRobot

JANUARY 20, 2022

Through the use of real-time datasets, machine learning, and wide-ranging AI capabilities, stakeholders across the enterprise including executives, clinicians, operational managers, and analysts will become more empowered to make forward-looking decisions faster.

Data Science 145

Data Science Data Warehouse Analytics Analytics 145

Dataconomy

SEPTEMBER 27, 2023

Data science and analytics MCSA and MCSE certifications can also lead to roles in data science and analytics, such as data analyst, data scientist, or business intelligence developer. Data analysts collect, clean, and analyze data to extract insights that can help businesses make better decisions.

Database Administration 113

Database Administration Database Azure Cloud Computing 113

AWS Machine Learning Blog

OCTOBER 9, 2024

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required.

AWS 93

AWS ML ML Data Quality 93

AWS Machine Learning Blog

JUNE 25, 2024

To create and share customer feedback analysis without the need to manage underlying infrastructure, Amazon QuickSight provides a straightforward way to build visualizations, perform one-time analysis, and quickly gain business insights from customer feedback, anytime and on any device. The LLM generates output based on the user prompt.

AWS 127

AWS Natural Language Processing Machine Learning Machine Learning 127

Mlearning.ai

FEBRUARY 16, 2023

Today, companies are facing a continual need to store tremendous volumes of data. The demand for information repositories enabling business intelligence and analytics is growing exponentially, giving birth to cloud solutions. Snowflake data warehouses deliver greater capacity without the need for any additional equipment.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Tableau

JUNE 8, 2021

We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models. Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

ODSC - Open Data Science

FEBRUARY 8, 2023

Common databases appear unable to cope with the immense increase in data volumes. This is where the BigQuery data warehouse comes into play. BigQuery operation principles Business intelligence projects presume collecting information from different sources into one database. You only pay for the resources you use.

Database 97

Database Data Science Big Data Big Data 97

IBM Journey to AI blog

SEPTEMBER 11, 2023

Don Haderle, a retired IBM Fellow and considered to be the “father of Db2,” viewed 1988 as a seminal point in its development as D B2 version 2 proved it was viable for online transactional processing (OLTP)—the lifeblood of business computing at the time. Db2 (LUW) was born in 1993, and 2023 marks its 30th anniversary.

Database 101

Database SQL Data Warehouse Machine Learning 101

IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

AI 74

AI AI Machine Learning Machine Learning 74

Smart Data Collective

FEBRUARY 20, 2022

What are the ties between DAM and data loss prevention (DLP) systems? What is the role of machine learning in monitoring database activity? Another direction in the progress of database monitoring systems is the interoperability with so-called data warehouses, which are increasingly popular among corporate customers.

Database 130

Database Machine Learning Machine Learning Data Warehouse 130

IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML). It can be used with both on-premise and multi-cloud environments.

AI 88

AI AI Data Warehouse ML 88

The MLOps Blog

MAY 17, 2023

Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one.

ETL 59

ETL Data Pipeline ML ML 59

IBM Journey to AI blog

JANUARY 18, 2023

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. A read-optimized platform that can integrate data from multiple applications emerged.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

Pickl AI

JULY 3, 2023

What is Business Intelligence? Business Intelligence (BI) refers to the technology, techniques, and practises that are used to gather, evaluate, and present information about an organisation in order to assist decision-making and generate effective administrative action. billion in 2015 and reached around $26.50

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

IBM Journey to AI blog

APRIL 8, 2024

Answer : Yes, Amazon RDS for Db2 can support analytics workloads, but it is not a data warehouse. Amazon RDS  Amazon RDS for Db2 supports single-node transactional, mixed and analytics workloads.    Scalability 5.

AWS 87

AWS Database Data Warehouse Analytics 87

IBM Journey to AI blog

JULY 11, 2023

Data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics that enable faster decision making and insights.

Data Warehouse 60

Data Warehouse Database Cloud Data Big Data 60