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 144

Data Mining Data Mining Data Mining Data Science 144

DataRobot

JANUARY 20, 2022

— Snowflake and DataRobot AI Cloud Platform is built around the need to enable secure and efficient data sharing, the integration of disparate data sources, and the enablement of intuitive operational and clinical predictive analytics. Building data communities.

Data Science 145

Data Science Data Warehouse Analytics Analytics 145

IBM Journey to AI blog

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.

AWS 95

AWS AI AI Data Warehouse 95

Dataconomy

MAY 10, 2023

Predictive analytics: Open source BI software can use algorithms and machine learning to analyze historical data and identify patterns that can be used to predict future trends and outcomes. BIDW: What makes business intelligence and data warehouses inseparable?

Business Intelligence 178

Business Intelligence Business Intelligence Power BI Data Analysis 178

Dataconomy

APRIL 27, 2023

AI computers can be programmed to perform a wide range of tasks, from natural language processing and image recognition to predictive analytics and decision-making. An alternative to manually annotating images for this activity is to use automated picture annotation.

Natural Language Processing 203

Natural Language Processing AI AI Artificial Intelligence 203

IBM Journey to AI blog

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 94

Data Science Analytics Analytics Data Scientist 94

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.

Machine Learning 105

Machine Learning Machine Learning ML ML 105

How to Learn Machine Learning

APRIL 26, 2025

So, they very often work with data engineers, analysts, and business partners to achieve that. Data engineers will also deal with the matters of data architecture and design of databases concerning the basic problem of how is data stored, structured, and accessed.

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

IBM Journey to AI blog

SEPTEMBER 11, 2023

enhances data management through automated insights generation, self-tuning performance optimization and predictive analytics. Db2 Warehouse SaaS, on the other hand, is a fully managed elastic cloud data warehouse with our columnar technology.

Database 101

Database SQL Data Warehouse Machine Learning 101

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

DECEMBER 7, 2023

Today, OLAP database systems have become comprehensive and integrated data analytics platforms, addressing the diverse needs of modern businesses. They are seamlessly integrated with cloud-based data warehouses, facilitating the collection, storage and analysis of data from various sources.

Data Preparation 72

Data Preparation Database Data Analysis Data Analysis 72

phData

MAY 4, 2023

KNIME Analytics Platform is an open-source platform that provides a suite of tools for data analytics, including data cleaning, machine learning, and predictive modeling. Snowflake is a cloud-based data warehouse that provides fast, secure, and scalable data storage and processing.

Predictive Analytics 52

Predictive Analytics Machine Learning Machine Learning Data Warehouse 52

phData

DECEMBER 12, 2023

We are going to break down what we know about Victory Vicky based on all the data sources we have moved into our data warehouse. The loyalty program is located in the MarTech Stack and moves data effortlessly into the data warehouse. This information is also funneled into the data warehouse.

Data Warehouse 52

Data Warehouse Machine Learning Machine Learning Tableau 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.

Data Lakes 75

Data Lakes Hadoop Data Silos Data Warehouse 75

IBM Journey to AI blog

JULY 17, 2023

It is supported by querying, governance, and open data formats to access and share data across the hybrid cloud. Through workload optimization across multiple query engines and storage tiers, organizations can reduce data warehouse costs by up to 50 percent.

AI 74

AI AI Machine Learning Machine Learning 74

Pickl AI

JULY 23, 2023

Using the right data analytics techniques can help in extracting meaningful insight, and using the same to formulate strategies. The analytics techniques like descriptive analytics, predictive analytics, diagnostic analytics and others find application in diverse industries, including retail, healthcare, finance, and marketing.

Analytics 52

Analytics Analytics Predictive Analytics Data Analysis 52

AWS Machine Learning Blog

MAY 8, 2024

SageMaker Feature Store – By using a centralized repository for ML features, SageMaker Feature Store enhances data consumption and facilitates experimentation with validation data. Instead of directly ingesting data from the data warehouse, the required features for training and inference steps are taken from the feature store.

ML 124

ML ML AWS AI 124

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. Data Lakes: These store raw, unprocessed data in its original format.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

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.

Data Governance 52

Data Governance Data Science Data Warehouse Analytics 52

Pickl AI

JANUARY 15, 2025

ETL (Extract, Transform, Load) Tools ETL tools are crucial for data integration processes. They extract data from various sources, transform it into a suitable format, and load it into a target database or data warehouse for analysis. How Do I Choose the Right BI Tool for My Organization?

Business Intelligence 52

Business Intelligence Business Intelligence Power BI Data Visualization 52

phData

APRIL 18, 2023

Faced with these challenges, asset servicers have acquired numerous technologies over time to meet their risk management, fund analytics, and settlement needs, leading to data fragmentation and inheriting complex data flows.

Data Silos 52

Data Silos ETL Clustering Analytics 52

Pickl AI

SEPTEMBER 18, 2024

It utilises Amazon Web Services (AWS) as its main data lake, processing over 550 billion events daily—equivalent to approximately 1.3 petabytes of data. The architecture is divided into two main categories: data at rest and data in motion.

Big Data 52

Big Data Big Data Apache Kafka Big Data Analytics 52

IBM Journey to AI blog

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 70

Machine Learning Machine Learning Data Science Big Data 70

Pickl AI

FEBRUARY 21, 2023

It involves using statistical and computational techniques to identify patterns and trends in the data that are not readily apparent. Data mining is often used in conjunction with other data analytics techniques, such as machine learning and predictive analytics, to build models that can be used to make predictions and inform decision-making.

Data Mining 52

Data Mining Data Mining Data Mining Data Scientist 52

Pickl AI

JULY 29, 2024

Log Analysis These are well-suited for analysing log data from various sources, such as web servers, application logs, and sensor data, to gain insights into user behaviour and system performance. Integration with Existing Systems Integrating a Hadoop cluster with existing data processing systems and applications can be complex.

Hadoop 52

Hadoop Clustering Big Data Big Data 52

Alation

JANUARY 27, 2022

Raw data includes market research, sales data, customer transactions, and more. Analytics can identify patterns that depict risks, opportunities, and trends. And historical data can be used to inform predictive analytic models, which forecast the future. What Is the Value of Analytics?

Data Governance 52

Data Governance Analytics Analytics Data Quality 52

Pickl AI

JULY 3, 2023

Writing technical documents on database content Mapping the various databases used in an organisation Developing, designing and analysing data architecture and data warehouses. BI Developer Skills Required To excel in this role, BI Developers need to possess a range of technical and soft skills.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

Pickl AI

DECEMBER 10, 2024

It ensures that businesses can process large volumes of data quickly, efficiently, and reliably. Whether managing transactional systems or handling massive data warehouses , Exadata guarantees seamless operations and top-tier reliability. Core Features Exadata delivers standout features tailored to enhance database performance.

Database 52

Database Analytics Analytics Business Intelligence 52

Data Science Blog

MARCH 3, 2023

der Aufbau einer Datenplattform, vielleicht ein Data Warehouse zur Datenkonsolidierung, Process Mining zur Prozessanalyse oder Predictive Analytics für den Aufbau eines bestimmten Vorhersagesystems, KI zur Anomalieerkennung oder je nach Ziel etwas ganz anderes.

Data Warehouse 147

Data Warehouse Data Lakes Data Engineer Data Engineering 147

phData

SEPTEMBER 27, 2024

Here’s how a composable CDP might incorporate the modeling approaches we’ve discussed: Data Storage and Processing : This is your foundation. You might choose a cloud data warehouse like the Snowflake AI Data Cloud or BigQuery. It’s like turning your data warehouse into a data distribution center.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

ODSC - Open Data Science

NOVEMBER 22, 2023

Data Version Control for Data Lakes: Handling the Changes in Large Scale In this article, we will delve into the concept of data lakes, explore their differences from data warehouses and relational databases, and discuss the significance of data version control in the context of large-scale data management.

Data Lakes 52

Data Lakes Predictive Analytics AI AI 52

Pickl AI

JANUARY 19, 2025

Data mining employs statistical techniques for predictive analytics. What is Data Warehousing? Data warehousing refers to the process of collecting, storing, and managing large volumes of structured data from various sources in a central repository known as a data warehouse.

Data Mining 52

Data Mining Data Mining Data Mining Data Warehouse 52