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

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

Analytics Vidhya

FEBRUARY 27, 2023

Introduction Enterprises here and now catalyze vast quantities of data, which can be a high-end source of business intelligence and insight when used appropriately. Delta Lake allows businesses to access and break new data down in real time.

Data Lakes 315

Data Lakes Business Intelligence Business Intelligence Analytics 315

Smart Data Collective

APRIL 15, 2021

Business Intelligence is the practice of collecting and analyzing data and transforming it into useful, actionable information. In order to make good business decisions, leaders need accurate insights into both the market and day-to-day operations. Set Up Data Integration. What kinds of BI tools are available ?

Business Intelligence 108

Business Intelligence Business Intelligence Analytics Analytics 108

Smart Data Collective

JULY 22, 2019

Big or small, every business needs good tools to analyze data and develop the most suitable business strategy based on the information they get. Business intelligence tools are means that help companies get insights from their data and get a better understanding of what directions and trends to follow.

Business Intelligence 103

Business Intelligence Business Intelligence Power BI Tableau 103

Analytics Vidhya

DECEMBER 23, 2022

Source: [link] Introduction In today’s digital world, data is generated at a swift pace. Data in itself is not useful unless we present it in a meaningful way and derive insights that help in making key business decisions. Business Intelligence (BI) tools serve the […].

Business Intelligence 271

Business Intelligence Business Intelligence Data Science Analytics 271

ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. Understanding Data Lakes A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

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

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

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

Tableau

JUNE 8, 2021

In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.

Tableau 102

Tableau Data Lakes Data Warehouse SQL 102

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

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

Women in Big Data

DECEMBER 9, 2024

Introduction In the rapidly evolving landscape of data analytics, Business Intelligence (BI) tools have become indispensable for organizations seeking to leverage their big data stores for strategic decision-making. The Tableau Prep Builder helps clean, shape, and combine data from multiple sources.

Big Data 52

Big Data Big Data Power BI Tableau 52

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

Smart Data Collective

SEPTEMBER 26, 2021

Working with massive structured and unstructured data sets can turn out to be complicated. It’s obvious that you’ll want to use big data, but it’s not so obvious how you’re going to work with it. So, let’s have a close look at some of the best strategies to work with large data sets.

Database 130

Database Data Visualization Big Data Big Data 130

Smart Data Collective

JUNE 16, 2022

Online analytical processing is a computer method that enables users to retrieve and query data rapidly and carefully in order to study it from a variety of angles. Trend analysis, financial reporting, and sales forecasting are frequently aided by OLAP business intelligence queries. ( see more ).

Analytics 139

Analytics Analytics Data Scientist Data Warehouse 139

Smart Data Collective

JULY 9, 2023

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses. What is ETL? Let’s break down each step: 1.

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ETL Data Quality Data Warehouse Analytics 105

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. Big Data here is a fundamental part of the scenario as it enables the technical integration of data from all digital environments along the customer path.

Database 97

Database Data Science Big Data Big Data 97

How to Learn Machine Learning

APRIL 26, 2025

Data Storage and Management Once data have been collected from the sources, they must be secured and made accessible. The responsibilities of this phase can be handled with traditional databases (MySQL, PostgreSQL), cloud storage (AWS S3, Google Cloud Storage), and big data frameworks (Hadoop, Apache Spark).

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 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. Business intelligence (BI) platforms. Better Data Culture.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Smart Data Collective

APRIL 29, 2020

The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated. Data analytics and visualisation.

Data Warehouse 98

Data Warehouse SQL Azure ETL 98

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Tableau

JUNE 8, 2021

In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

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

IBM Journey to AI blog

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 Lakes Data Warehouse Big Data 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes. And you should have experience working with big data platforms such as Hadoop or Apache Spark.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Pickl AI

JULY 29, 2024

It utilises the Hadoop Distributed File System (HDFS) and MapReduce for efficient data management, enabling organisations to perform big data analytics and gain valuable insights from their data. In a Hadoop cluster, data stored in the Hadoop Distributed File System (HDFS), which spreads the data across the nodes.

Hadoop 52

Hadoop Clustering Big Data Big Data 52

IBM Journey to AI blog

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Sometimes, these outputs are biased because the data used to train the model was incomplete or inaccurate in some way.

AI 93

AI AI Data Scientist Data Governance 93

Alation

FEBRUARY 13, 2020

For the second year in a row, Dresner Advisory Services’ Wisdom of Crowds® Data Catalog Market Study 7 named Alation as the top-ranked data cataloging solution. Data catalogs are proving to be key to the success of business intelligence efforts and important pieces of a enterprises overall data governance strategy.”.

Data Governance 52

Data Governance Machine Learning Machine Learning Analytics 52

phData

JULY 10, 2024

Sigma Computing is a cloud-based business intelligence and analytics tool for collaborative data exploration, analysis, and visualization. Unlike traditional BI tools, its user-friendly interface ensures that users of all technical levels can seamlessly interact with data. Choose your desired data source type (e.g.,

SQL 52

SQL Data Warehouse Analytics Analytics 52

IBM Journey to AI blog

SEPTEMBER 17, 2024

Performance benchmarks Our in-memory TM1 engine rapidly analyzes big data, delivering real-time insights and AI-powered forecasting for faster, more accurate planning. Data updates are processed instantly, reflecting changes in real time and handling millions of rows per second, so decision-makers have up-to-date information.

Analytics 40

Analytics Analytics Data Warehouse Database 40

IBM Journey to AI blog

DECEMBER 16, 2022

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

Precisely

APRIL 3, 2023

It simply wasn’t practical to adopt an approach in which all of an organization’s data would be made available in one central location, for all-purpose business analytics. To speed analytics, data scientists implemented pre-processing functions to aggregate, sort, and manage the most important elements of the data.

ETL 52

ETL Analytics Analytics Database 52

Alation

APRIL 14, 2022

Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. A cloud environment with such features will support collaboration across departments and across common data types, including csv, JSON, XML, AVRO, Parquet, Hyper, TDE, and more. On-premises business intelligence and databases.

Data Governance 59

Data Governance ML ML Cloud Data 59

Pickl AI

APRIL 26, 2024

Data Warehousing and ETL Processes What is a data warehouse, and why is it important? A data warehouse is a centralised repository that consolidates data from various sources for reporting and analysis. It is essential to provide a unified data view and enable business intelligence and analytics.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

DagsHub

NOVEMBER 11, 2024

Creating multimodal embeddings means training models on datasets with multiple data types to understand how these types of information are related. Multimodal embeddings help combine unstructured data from various sources in data warehouses and ETL pipelines.

AI 52

AI AI Data Lakes Database 52