Analytics Vidhya

DECEMBER 28, 2022

This article was published as a part of the Data Science Blogathon. Source: [link] Introduction If you are familiar with databases, or data warehouses, you have probably heard the term “ETL.” As the amount of data at organizations grow, making use of that data in analytics to derive business insights grows as well.

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ETL AWS Data Warehouse Data Science 212

Dataconomy

APRIL 27, 2023

What is an online transaction processing database (OLTP)? OLTP is the backbone of modern data processing, a critical component in managing large volumes of transactions quickly and efficiently. This approach allows businesses to efficiently manage large amounts of data and leverage it to their advantage in a highly competitive market.

Database 159

Database Data Scientist Data Mining Data Mining 159

Pickl AI

FEBRUARY 23, 2023

Organisations must store data in a safe and secure place for which Databases and Data warehouses are essential. You must be familiar with the terms, but Database and Data Warehouse have some significant differences while being equally crucial for businesses. What is a Database?

Data Warehouse 52

Data Warehouse Database Data Analysis Data Analysis 52

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

MAY 16, 2022

Data Management is considered to be a core function of any organization. Data management software helps in reducing the cost of maintaining the data by helping in the management and maintenance of the data stored in the database. There are various types of data management systems available.

Data Warehouse 136

Data Warehouse Data Mining Data Mining Data Mining 136

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

Pickl AI

FEBRUARY 3, 2025

Summary: A Data Warehouse consolidates enterprise-wide data for analytics, while a Data Mart focuses on department-specific needs. Data Warehouses offer comprehensive insights but require more resources, whereas Data Marts provide cost-effective, faster access to focused data.

Data Warehouse 52

Data Warehouse Analytics Analytics Database 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

Flipboard

DECEMBER 11, 2024

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Complete the following steps: On the project page, choose Data.

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SQL AWS Data Lakes ML 160

Smart Data Collective

DECEMBER 13, 2022

Five Best Practices for Data Analytics. Extracted data must be saved someplace. There are several choices to consider, each with its own set of advantages and disadvantages: Data warehouses are used to store data that has been processed for a specific function from one or more sources. Select a Storage Platform.

Analytics 145

Analytics Analytics Data Analysis Data Analysis 145

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.

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Data Pipeline Data Warehouse ETL Data Lakes 145

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. These tools will help make your initial data exploration process easy.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

Smart Data Collective

JULY 9, 2023

ETL is a three-step process that involves extracting data from various sources, transforming it into a consistent format, and loading it into a target database or data warehouse. Extract The extraction phase involves retrieving data from diverse sources such as databases, spreadsheets, APIs, or other systems.

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ETL Data Quality Data Warehouse Analytics 105

AWS Machine Learning Blog

DECEMBER 29, 2022

Many of the RStudio on SageMaker users are also users of Amazon Redshift , a fully managed, petabyte-scale, massively parallel data warehouse for data storage and analytical workloads. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools.

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AWS Machine Learning Machine Learning Database 120

Smart Data Collective

SEPTEMBER 26, 2021

There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. The raw data can be fed into a database or data warehouse. If it’s not done right away, then later.

Database 130

Database Data Visualization Big Data Big Data 130

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.

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Data Warehouse SQL Azure ETL 98

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

IBM Journey to AI blog

DECEMBER 7, 2023

Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. Defining OLAP today OLAP database systems have significantly evolved since their inception in the early 1990s.

Data Preparation 72

Data Preparation Database Data Analysis Data Analysis 72

Dataconomy

JUNE 1, 2023

This open format allows for seamless storage and retrieval of data across different databases. By automating the integration of all Fabric workloads into OneLake, Microsoft eliminates the need for developers, analysts, and business users to create their own data silos.

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Power BI Data Lakes Azure Data Silos 194

AWS Machine Learning Blog

APRIL 16, 2024

Solution overview With SageMaker Studio JupyterLab notebook’s SQL integration, you can now connect to popular data sources like Snowflake, Athena, Amazon Redshift, and Amazon DataZone. For example, you can visually explore data sources like databases, tables, and schemas directly from your JupyterLab ecosystem.

SQL 107

SQL AWS Database Data Scientist 107

phData

NOVEMBER 28, 2024

“ Vector Databases are completely different from your cloud data warehouse.” – You might have heard that statement if you are involved in creating vector embeddings for your RAG-based Gen AI applications. Are you interested in exploring Snowflake as a vector database?

Python 52

Python Database SQL Machine Learning 52

Pickl AI

OCTOBER 6, 2024

It is a crucial data integration process that involves moving data from multiple sources into a destination system, typically a data warehouse. This process enables organisations to consolidate their data for analysis and reporting, facilitating better decision-making.

ETL 52

ETL Data Warehouse Data Quality Data Lakes 52

IBM Journey to AI blog

JANUARY 10, 2023

Netezza Performance Server (NPS) has recently added the ability to access Parquet files by defining a Parquet file as an external table in the database. This allows data that exists in cloud object storage to be easily combined with existing data warehouse data without data movement. The data definition.

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Data Warehouse Data Analysis Data Analysis 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.

Database 97

Database Data Science Big Data Big Data 97

ODSC - Open Data Science

JUNE 12, 2023

They all agree that a Datamart is a subject-oriented subset of a data warehouse focusing on a particular business unit, department, subject area, or business functionality. The Datamart’s data is usually stored in databases containing a moving frame required for data analysis, not the full history of data.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

phData

MAY 12, 2023

In today’s data-driven world, industries across the board are turning to advanced tools and technologies to gain deeper insights and improve their decision-making processes. This is particularly true in the financial services sector, where accurate, real-time data analysis can be the key to success. Oh–and it’s free.

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Data Warehouse Machine Learning Machine Learning Database 72

Women in Big Data

OCTOBER 9, 2024

The success of any data initiative hinges on the robustness and flexibility of its big data pipeline. What is a Data Pipeline? A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

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

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Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

JULY 25, 2024

Must Read Blogs: Exploring the Power of Data Warehouse Functionality. Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world. Exploring Differences: Database vs Data Warehouse. It is commonly used in data warehouses for business analytics and reporting.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

phData

JANUARY 5, 2023

On-Premises to The Cloud This type of migration involves moving an organization’s BI platform from an on-premises environment (such as a local server or data center) to a cloud-based environment. For example, suppose an organization moves from an on-premises database to a cloud-based database like Snowflake.

Analytics 52

Analytics Analytics Tableau Database 52

Pickl AI

MARCH 3, 2025

Essentially, BI bridges the gap between raw data and actionable knowledge. It gathers information from various sources sales databases, marketing platforms, customer feedback, and more and consolidates it into a unified view. Ensuring data accuracy and consistency through cleansing and validation processes.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analyst Data Visualization 52

phData

SEPTEMBER 19, 2023

A data lake is a centralized repository containing extensive storage for raw, unfiltered data coming into a company’s data storage system. This data can be structured, semi-structured, or unstructured and comes from various sources such as databases, IoT devices, log files, etc.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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.

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Data Science Analytics Analytics Data Scientist 94

AWS Machine Learning Blog

JUNE 25, 2024

The raw data is processed by an LLM using a preconfigured user prompt. The processed output is stored in a database or data warehouse, such as Amazon Relational Database Service (Amazon RDS). The stored data is visualized in a BI dashboard using QuickSight. The LLM generates output based on the user prompt.

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Pickl AI

JULY 25, 2024

It’s the critical process of capturing, transforming, and loading data into a centralised repository where it can be processed, analysed, and leveraged. Data Ingestion Meaning At its core, It refers to the act of absorbing data from multiple sources and transporting it to a destination, such as a database, data warehouse, or data lake.

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Apache Kafka Data Lakes Data Warehouse Data Quality 52

Dataconomy

MARCH 24, 2023

BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions. A career path in BI can be a lucrative and rewarding choice for those with interest in data analysis and problem-solving. How to become a blockchain maestro?

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70