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. As data lakes gain prominence as a preferred solution for storing and processing enormous datasets, the need for effective data version control mechanisms becomes increasingly evident.

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Data Lakes Data Warehouse Database Big Data 98

Data Science Dojo

JULY 6, 2023

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. Google BigQuery: Google BigQuery is a serverless, cloud-based data warehouse designed for big data analytics.

Data Engineering 195

Data Engineering Data Engineering Data Engineering Data Engineer 195

Flipboard

DECEMBER 18, 2023

Amazon Redshift powers data-driven decisions for tens of thousands of customers every day with a fully managed, AI-powered cloud data warehouse, delivering the best price-performance for your analytics workloads. Learn more about the AWS zero-ETL future with newly launched AWS databases integrations with Amazon Redshift.

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AWS Data Warehouse ETL SQL 138

Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. In this article, we’ll focus on a data lake vs. data warehouse.

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Data Warehouse Data Lakes Hadoop Big Data 59

Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. It will enable you to quickly transform and load the data results into Amazon S3 data lakes or JDBC data stores.

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Apache Kafka ETL Data Lakes AWS 130

Pickl AI

APRIL 9, 2025

Summary: This guide explores the top list of ETL tools, highlighting their features and use cases. It provides insights into considerations for choosing the right tool, ensuring businesses can optimize their data integration processes for better analytics and decision-making. What is ETL? What are ETL Tools?

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

Pickl AI

OCTOBER 6, 2024

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. What is ETL? ETL stands for Extract, Transform, and Load.

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ETL Data Warehouse Data Quality 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.

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Data Lakes Data Warehouse Database ETL 52

Flipboard

DECEMBER 11, 2024

Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data.

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SQL AWS Data Lakes AI 159

phData

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake? Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

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. March 30, 2021.

Azure 102

Azure Tableau Data Lakes SQL 102

Pickl AI

OCTOBER 17, 2024

Summary: The ETL process, which consists of data extraction, transformation, and loading, is vital for effective data management. Following best practices and using suitable tools enhances data integrity and quality, supporting informed decision-making. Introduction The ETL process is crucial in modern data management.

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ETL Data Warehouse SQL Data Quality 52

Smart Data Collective

OCTOBER 17, 2022

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. Destination.

Data Pipeline 145

Data Pipeline Data Warehouse ETL Data Lakes 145

The MLOps Blog

MAY 17, 2023

However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.

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ODSC - Open Data Science

SEPTEMBER 29, 2023

Data management problems can also lead to data silos; disparate collections of databases that don’t communicate with each other, leading to flawed analysis based on incomplete or incorrect datasets. One way to address this is to implement a data lake: a large and complex database of diverse datasets all stored in their original format.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

phData

NOVEMBER 9, 2023

A data warehouse is a centralized and structured storage system that enables organizations to efficiently store, manage, and analyze large volumes of data for business intelligence and reporting purposes. What is a Data Lake? What is the Difference Between a Data Lake and a Data Warehouse?

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

IBM Journey to AI blog

SEPTEMBER 25, 2023

But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. Every day, millions of riders use the Uber app, unwittingly contributing to a complex web of data-driven decisions. Consider the magnitude of Uber’s footprint.

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Data Lakes Analytics Analytics Clustering 102

Flipboard

NOVEMBER 27, 2024

He helps architect solutions across AI/ML applications, enterprise data platforms, data governance, and unified search in enterprises. Gi Kim is a Data & ML Engineer with the AWS Professional Services team, helping customers build data analytics solutions and AI/ML applications.

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Flipboard

JUNE 26, 2023

Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. Typically, companies ingest data from multiple sources into their data lake to derive valuable insights from the data. This will open the ML transforms page.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines.

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Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

Analytics 69

Analytics Analytics Data Silos Data Lakes 69

AWS Machine Learning Blog

FEBRUARY 21, 2025

Thats why we use advanced technology and data analytics to streamline every step of the homeownership experience, from application to closing. This also led to a backlog of data that needed to be ingested. Analytic data is stored in Amazon Redshift. Applying for a mortgage can be complex and time-consuming.

Data Science 84

Data Science AWS Hadoop Data Scientist 84

Women in Big Data

OCTOBER 9, 2024

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. Once ingested, the data is prepared through filtering, error correction, and restructuring for ease of use.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

IBM Journey to AI blog

MARCH 14, 2024

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. It enables secure data sharing for analytics and AI across your ecosystem.

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ODSC - Open Data Science

APRIL 3, 2023

As the sibling of data science, data analytics is still a hot field that garners significant interest. Companies have plenty of data at their disposal and are looking for people who can make sense of it and make deductions quickly and efficiently.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Women in Big Data

NOVEMBER 27, 2024

It consolidates data from various systems, such as transactional databases, CRM platforms, and external data sources, enabling organizations to perform complex queries and derive insights. By maintaining historical data from disparate locations, a data warehouse creates a foundation for trend analysis and strategic decision-making.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Flipboard

MARCH 7, 2023

Then the transcripts of contacts become available to CSBA to extract actionable insights through millions of customer contacts for the sellers, and the data is stored in the Seller Data Lake. Contact Lens then stores analytics data into an Amazon Simple Storage Service (Amazon S3) bucket for long-term retention.

ML 123

ML ML AWS AI 123

Tableau

MARCH 29, 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. March 30, 2021.

Azure 52

Azure Tableau Data Lakes SQL 52

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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AWS Data Lakes Clustering Data Preparation 98

IBM Journey to AI blog

JANUARY 18, 2023

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. Data lakehouse was created to solve these problems.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

Dataversity

MARCH 17, 2021

I do not think it is an exaggeration to say data analytics has come into its own over the past decade or so. What started out as an attempt to extract business insights from transactional data in the ’90s and early 2000s has now transformed into an […]. appeared first on DATAVERSITY.

Analytics 52

Analytics Analytics Data Lakes Data Warehouse 52

IBM Journey to AI blog

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

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Data Lakes AI AI Data Governance 73

AWS Machine Learning Blog

JUNE 18, 2024

LLMs excel at writing code and reasoning over text, but tend to not perform as well when interacting directly with time-series data. The output data is transformed to a standardized format and stored in a single location in Amazon S3 in Parquet format, a columnar and efficient storage format.

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AWS AI AI Python 135

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

AI 131

AI AI ML ML 131

phData

AUGUST 9, 2023

In short, ELT exemplifies the data strategy required in the era of big data, cloud, and agile analytics. With ELT, we first extract data from source systems, then load the raw data directly into the data warehouse before finally applying transformations natively within the data warehouse.

ETL 52

ETL Data Warehouse Cloud Data Big Data 52

AWS Machine Learning Blog

AUGUST 2, 2024

This is a guest post co-written with Vicente Cruz Mínguez, Head of Data and Advanced Analytics at Cepsa Química, and Marcos Fernández Díaz, Senior Data Scientist at Keepler. About the authors Vicente Cruz Mínguez is the Head of Data & Advanced Analytics at Cepsa Química.

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AWS Machine Learning Machine Learning Database 131

Pickl AI

JANUARY 28, 2025

Research indicates that companies utilizing advanced analytics are 5 times more likely to make faster decisions than their competitors. Key Components of Business Intelligence Architecture Business Intelligence (BI) architecture is a structured framework that enables organizations to gather, analyze, and present data effectively.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52