Data Science Dojo

SEPTEMBER 11, 2024

These experiences facilitate professionals from ingesting data from different sources into a unified environment and pipelining the ingestion, transformation, and processing of data to developing predictive models and analyzing the data by visualization in interactive BI reports.

Power BI 337

Power BI Data Pipeline Data Warehouse Data Engineering 337

AWS Machine Learning Blog

OCTOBER 28, 2024

The workflow includes the following steps: Within the SageMaker Canvas interface, the user composes a SQL query to run against the GCP BigQuery data warehouse. Athena returns the queried data from BigQuery to SageMaker Canvas, where you can use it for ML model training and development purposes within the no-code interface.

Machine Learning 115

Machine Learning Machine Learning ML ML 115

AWS Machine Learning Blog

APRIL 16, 2024

In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference.

SQL 130

SQL AWS Database Data Scientist 130

Flipboard

JANUARY 24, 2025

RAG data store The Retrieval Augmented Generation (RAG) data store delivers up-to-date, precise, and access-controlled knowledge from various data sources such as data warehouses, databases, and other software as a service (SaaS) applications through data connectors.

AWS 155

AWS AI AI Data Warehouse 155

Smart Data Collective

JUNE 15, 2021

How companies gather, manage and control data has undeniably become one of the most important aspects of business success today. It’s also possible to employ extra caching or materialized views in the data warehouse in addition to caching in Looker (depending on the capability of your data warehouse). Final word.

Data Warehouse 126

Data Warehouse Database SQL Data Analyst 126

Smart Data Collective

APRIL 8, 2020

Furthermore, it has been estimated that by 2025, the cumulative data generated will triple to reach nearly 175 zettabytes. Demands from business decision makers for real-time data access is also seeing an unprecedented rise at present, in order to facilitate well-informed, educated business decisions.

Data Warehouse 144

Data Warehouse Big Data Big Data Big Data Analytics 144

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

AWS Machine Learning Blog

APRIL 3, 2025

Data is one of the most critical assets of many organizations. Theyre constantly seeking ways to use their vast amounts of information to gain competitive advantages. This enables OMRON to extract meaningful patterns and trends from its vast data repositories, supporting more informed decision-making at all levels of the organization.

AWS 90

AWS Data Governance Data Silos SQL 90

Data Science Connect

JANUARY 27, 2023

In this blog post, we will be discussing 7 tips that will help you become a successful data engineer and take your career to the next level. Learn SQL: As a data engineer, you will be working with large amounts of data, and SQL is the most commonly used language for interacting with databases.

Data Engineering 130

Data Engineering Data Engineering Data Engineer Data Engineering 130

IBM Journey to AI blog

APRIL 24, 2023

Why data warehousing is critical to a company’s success Data warehousing is the secure electronic information storage by a company or organization. Data is reported from one central repository, enabling management to draw more meaningful business insights and make faster, better decisions.

Data Warehouse 112

Data Warehouse Data Lakes Database Big Data 112

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

Analytics Vidhya

MARCH 10, 2023

Introduction Snowflake is a cloud-based data warehousing platform that enables enterprises to manage vast and complicated information by providing scalable storage and processing capabilities. It is intended to be a fully managed, multi-cloud solution that does not need clients to handle hardware or software.

Analytics 306

Analytics Analytics Data Warehouse Data Engineering 306

Flipboard

DECEMBER 6, 2023

In this post, we discuss a Q&A bot use case that Q4 has implemented, the challenges that numerical and structured datasets presented, and how Q4 concluded that using SQL may be a viable solution. Providing incorrect or outdated information can impact investors’ and shareholders’ trust, in addition to other possible data privacy risks.

SQL 167

SQL Database AWS Machine Learning 167

Smart Data Collective

JUNE 6, 2021

In the simplest of terms, the latter refers to a system that examines large bodies of data with the goal of uncovering trends, patterns, correlations and other helpful information. What is big data used for? Customer experience is another key area that can benefit from big data analytics. Big data analytics advantages.

Big Data Analytics 137

Big Data Analytics Big Data Analytics Big Data Big Data 137

Smart Data Collective

MAY 31, 2022

Data activation is a new and exciting way that businesses can think of their data. It’s more than just data that provides the information necessary to make wise, data-driven decisions. It’s more than just allowing access to data warehouses that were becoming dangerously close to data silos.

ETL 142

ETL Data Silos Data Warehouse Big Data 142

Tableau

MARCH 30, 2021

we’ve added new connectors to help our customers access more data in Azure than ever before: an Azure SQL Database connector and an Azure Data Lake Storage Gen2 connector. As our customers increasingly adopt the cloud, we continue to make investments that ensure they can access their data anywhere. Azure SQL Database.

Azure 102

Azure Tableau Data Lakes SQL 102

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

MARCH 24, 2025

Summary: Online Analytical Processing (OLAP) systems in Data Warehouse enable complex Data Analysis by organizing information into multidimensional structures. Key characteristics include fast query performance, interactive analysis, hierarchical data organization, and support for multiple users. What is OLAP?

Data Analysis 52

Data Analysis Data Analysis Database Analytics 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

phData

JULY 10, 2024

Sigma Computing , a cloud-based analytics platform, helps data analysts and business professionals maximize their data with collaborative and scalable analytics. One of Sigma’s key features is its support for custom SQL queries and CSV file uploads. These tools allow users to handle more advanced data tasks and analyses.

SQL 52

SQL Data Warehouse Analytics Analytics 52

AWS Machine Learning Blog

AUGUST 20, 2024

Natural language is ambiguous and imprecise, whereas data adheres to rigid schemas. For example, SQL queries can be complex and unintuitive for non-technical users. Handling complex queries involving multiple tables, joins, and aggregations makes it difficult to interpret user intent and translate it into correct SQL operations.

SQL 127

SQL AWS Database Natural Language Processing 127

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines. What Does a Data Engineer Do?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Dataconomy

OCTOBER 8, 2024

Accurate and secure data can help to streamline software engineering processes and lead to the creation of more powerful AI tools, but it has become a challenge to maintain the quality of the expansive volumes of data needed by the most advanced AI models.

Data Pipeline 132

Data Pipeline AI AI Data Warehouse 132

Tableau

APRIL 3, 2023

Madeleine Corneli Senior Manager, Product Management, Tableau Adiascar Cisneros Manager, Product Management, Tableau Bronwen Boyd April 3, 2023 - 5:27pm April 3, 2023 Google Cloud’s BigQuery is a serverless, highly-scalable cloud-based data warehouse solution that allows users to store, query, and analyze large datasets quickly.

Tableau 98

Tableau SQL Data Warehouse Database 98

Dataconomy

JUNE 1, 2023

Unified data storage : Fabric’s centralized data lake, Microsoft OneLake, eliminates data silos and provides a unified storage system, simplifying data access and retrieval. This transparency helps businesses control costs and make informed decisions about resource allocation.

Power BI 194

Power BI Data Lakes Azure Data Silos 194

Smart Data Collective

SEPTEMBER 26, 2021

Data is processed to generate information, which can be later used for creating better business strategies and increasing the company’s competitive edge. 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. Preserve information: Keep your raw data raw.

Database 130

Database Data Visualization Big Data Big Data 130

Smart Data Collective

JUNE 16, 2022

There are a lot of important queries that you need to run as a data scientist. This tool can be great for handing SQL queries and other data queries. Every data scientist needs to understand the benefits that this technology offers. The data is processed and modified after it has been extracted.

Analytics 139

Analytics Analytics Data Scientist Data Warehouse 139

How to Learn Machine Learning

APRIL 26, 2025

Data can be generated from databases, sensors, social media platforms, APIs, logs, and web scraping. Data can be in structured (like tables in databases), semi-structured (like XML or JSON), or unstructured (like text, audio, and images) form.

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

IBM Data Science in Practice

JANUARY 13, 2025

The blog post explains how the Internal Cloud Analytics team leveraged cloud resources like Code-Engine to improve, refine, and scale the data pipelines. Background One of the Analytics teams tasks is to load data from multiple sources and unify it into a data warehouse. Thus, it has only a minimal footprint.

ETL 100

ETL Data Pipeline Database Data Warehouse 100

AWS Machine Learning Blog

JUNE 13, 2023

The natural language capabilities allow non-technical users to query data through conversational English rather than complex SQL. The AI and language models must identify the appropriate data sources, generate effective SQL queries, and produce coherent responses with embedded results at scale.

Database 101

Database SQL AWS AI 101

AWS Machine Learning Blog

FEBRUARY 14, 2024

The foundation model then generates more relevant and accurate information. Amazon Redshift has announced a feature called Amazon Redshift ML that makes it straightforward for data analysts and database developers to create, train, and apply machine learning (ML) models using familiar SQL commands in Redshift data warehouses.

AWS 142

AWS AI AI Database 142

phData

MARCH 8, 2023

Fivetran, a cloud-based automated data integration platform, has emerged as a leading choice among businesses looking for an easy and cost-effective way to unify their data from various sources. Fivetran is used by businesses to centralize data from various sources into a single, comprehensive data warehouse.

Data Warehouse 96

Data Warehouse Data Engineering Data Engineering Data Engineer 96

Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. You can use query_string to filter your dataset by SQL and unload it to Amazon S3. If you’re familiar with SageMaker and writing Spark code, option B could be your choice.

ML 121

ML ML AWS Data Warehouse 121

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.

AWS 138

AWS Machine Learning Machine Learning Clustering 138

ODSC - Open Data Science

FEBRUARY 24, 2023

Data Quality Now that you’ve learned more about your data and cleaned it up, it’s time to ensure the quality of your data is up to par. With these data exploration tools, you can determine if your data is accurate, consistent, and reliable.

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

ODSC - Open Data Science

AUGUST 23, 2023

Run pandas at scale on your data warehouse Most enterprise data teams store their data in a database or data warehouse, such as Snowflake, BigQuery, or DuckDB. Ponder solves this problem by translating your pandas code to SQL that can be understood by your data warehouse.

Data Warehouse 52

Data Warehouse Data Science Database SQL 52