Precisely

MARCH 9, 2023

Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. It is often used as a foundation for enterprise data lakes.

Data Lakes 59

Data Lakes Data Warehouse Hadoop Apache Hadoop 59

Hacker News

MARCH 28, 2024

A unified SQL query interface and portable runtime to locally materialize, accelerate, and query data tables sourced from any database, data warehouse, or data lake. spiceai/spiceai

SQL 138

SQL Data Lakes Data Warehouse Database 138

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

Smart Data Collective

SEPTEMBER 23, 2020

While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Both data warehouses and data lakes are used when storing big data.

Data Lakes 105

Data Lakes Data Warehouse Big Data Big Data 105

insideBIGDATA

APRIL 22, 2024

In this contributed article, Sida Shen, product marketing manager, CelerData, discusses how data lakehouse architectures promise the combined strengths of data lakes and data warehouses, but one question arises: why do we still find the need to transfer data from these lakehouses to proprietary data warehouses?

Data Warehouse 195

Data Warehouse Data Lakes Big Data Big Data 195

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. The data lake can then refine, enrich, index, and analyze that data. and various countries in Europe.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

Analytics Vidhya

JULY 29, 2022

Introduction We are all pretty much familiar with the common modern cloud data warehouse model, which essentially provides a platform comprising a data lake (based on a cloud storage account such as Azure Data Lake Storage Gen2) AND a data warehouse compute engine […].

Azure 361

Azure Data Warehouse Data Lakes Analytics 361

Smart Data Collective

APRIL 2, 2019

Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings. Analytics Magazine notes that data lakes are among the most useful tools that an enterprise may have at its disposal when aiming to compete with competitors via innovation.

Data Lakes 83

Data Lakes Big Data Big Data Data Scientist 83

Hacker News

DECEMBER 29, 2023

In the past few years, I've heard a lot about Avro, Parquet, ORC, Arrow and Feather, but I also keep hearing about Iceberg and Delta Lake. As a "database person", I’ve been struggling to understand all of these different things, and how they relate to Data Lakes and Data

Data Lakes 138

Data Lakes Database 138

Dataversity

APRIL 23, 2021

Within the Data Management industry, it’s becoming clear that the old model of rounding up massive amounts of data, dumping it into a data lake, and building an API to extract needed information isn’t working. The post Why Graph Databases Are an Essential Choice for Master Data Management appeared first on DATAVERSITY.

Database 90

Database Data Lakes Data Silos Data Governance 90

Data Science Dojo

JANUARY 12, 2023

When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business?

Data Lakes 222

Data Lakes Data Warehouse Hadoop Machine Learning 222

Data Science Blog

MAY 15, 2023

tl;dr Ein Data Lakehouse ist eine moderne Datenarchitektur, die die Vorteile eines Data Lake und eines Data Warehouse kombiniert. Die Definition eines Data Lakehouse Ein Data Lakehouse ist eine moderne Datenspeicher- und -verarbeitungsarchitektur, die die Vorteile von Data Lakes und Data Warehouses vereint.

Data Warehouse 147

Data Warehouse Data Lakes Azure AWS 147

ODSC - Open Data Science

SEPTEMBER 12, 2023

Be sure to check out his talk, “ What is a Time-series Database and Why do I Need One? Most data scientists are familiar with the concept of time series data and work with it often. The time series database (TSDB) , however, is still an underutilized tool in the data science community. at ODSC West 2023.

Data Scientist 97

Data Scientist Database Data Lakes Data Science 97

Dataversity

MAY 19, 2023

In the early days of business analysis and underwriting, data was managed with simply a pen and paper and, of course, Excel spreadsheets. As technology has advanced, databases, warehouses, and data lakes have enabled information to be collected, stored, and managed electronically.

Data Lakes 52

Data Lakes Database Data Quality 52

Data Science Connect

JULY 28, 2023

Data Collection and Integration Data engineers are responsible for designing robust data collection systems that gather information from various IoT devices and sensors. This data is then integrated into centralized databases for further processing and analysis.

Internet of Things 130

Internet of Things Data Engineering Data Engineer Data Engineering 130

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

Smart Data Collective

FEBRUARY 23, 2022

The size and the variety of data that enterprises have to deal with have become more complex and larger. Traditional relational databases provide certain benefits, but they are not suitable to handle big and various data. In traditional relational database engines, users can plan indexing to improve performance.

Data Lakes 97

Data Lakes AWS SQL Database 97

The MLOps Blog

JANUARY 23, 2023

However, there are some key differences that we need to consider: Size and complexity of the data In machine learning, we are often working with much larger data. Basically, every machine learning project needs data. First of all, machine learning engineers and data scientists often use data from different data vendors.

ML 52

ML ML Data Lakes Machine Learning 52

ODSC - Open Data Science

MAY 31, 2023

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

Data Lakes 131

Data Lakes Machine Learning Machine Learning Apache Kafka 131

AWS Machine Learning Blog

JUNE 13, 2023

The combination of large language models (LLMs), including the ease of integration that Amazon Bedrock offers, and a scalable, domain-oriented data infrastructure positions this as an intelligent method of tapping into the abundant information held in various analytics databases and data lakes.

Database 72

Database SQL AWS AI 72

Dataversity

MARCH 12, 2021

blog series, we experiment with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT […]. In the “Will They Blend?”

Data Lakes 52

Data Lakes SQL Database Data Science 52

Hacker News

JANUARY 26, 2024

TL;DR: DuckDB can attach MySQL, Postgres, and SQLite databases in addition to databases stored in its own format. This allows data to be read into DuckDB and moved between these systems in a convenient manner. In modern data analysis, data must often be combined from a wide variety of different sources.

Database 169

Database Data Analysis Data Analysis Data Lakes 169

Smart Data Collective

FEBRUARY 20, 2022

Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps. This article finally gets to the core question we started with: what can AWS do for your SaaS business?

AWS 114

AWS Cloud Computing Data Lakes Database 114

Tableau

MAY 11, 2022

Certified data sources carefully chosen by site administrators and project leaders. Recommended data sources personally certified and/or automatically selected based on organizational usage patterns. Recommended database tables that are used frequently in data sources and workbooks published to your Tableau server.

Tableau 94

Tableau Data Warehouse Database Data Analyst 94

Tableau

MAY 11, 2022

Certified data sources carefully chosen by site administrators and project leaders. Recommended data sources personally certified and/or automatically selected based on organizational usage patterns. Recommended database tables that are used frequently in data sources and workbooks published to your Tableau server.

Tableau 92

Tableau Data Warehouse Database Data Analyst 92

phData

SEPTEMBER 26, 2023

Sources The sources involved could influence or determine the options available for the data ingestion tool(s). These could include other databases, data lakes, SaaS applications (e.g. Salesforce), Access databases, SharePoint, or Excel spreadsheets. Data flows from the current data platform to the destination.

SQL 52

SQL Database ETL Data Modeling 52

phData

MARCH 22, 2024

Snowflake-managed Iceberg table’s performance is at par with Snowflake native tables while storing the data in public cloud storage. They are Ideal for situations where the data is already stored in data lakes and do not intend to load into Snowflake but need to use the features and performance of Snowflake.

SQL 52

SQL AWS Database Data Lakes 52

Dataversity

MAY 7, 2021

blog series, we experiment with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source DevOps on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT […]. In the “Will They Blend?”

Data Lakes 52

Data Lakes SQL Database Data Science 52

SAS Software

NOVEMBER 29, 2023

Azure Data Lake Storage (ADLS) Gen2のストレージアカウントの作成 3-2.ストレージアカウントのデータストレージコンテナの作成 Bulkload機能について 3.BULKLOAD機能を利用するためのAzure側で必要なサービスの作成 BULKLOAD機能を利用するためのAzure側で必要なサービスの作成 3-1.Azure ストレージアカウントのデータストレージコンテナの作成 3-3.ストレージアカウントの利用ユーザー権限の設定 ストレージアカウントの利用ユーザー権限の設定 3-4.データ書き込み用のSASコードの実行 データ書き込み用のSASコードの実行 3-5.Azureアプリの設定 Azureアプリの設定 4.SAS

Azure 57

Azure Data Lakes SQL Analytics 57

Data Science 101

DECEMBER 16, 2019

Azure Data Factory Preserves Metadata during File Copy When performing a File copy between Amazon S3, Azure Blob, and Azure Data Lake Gen 2, the metadata will be copied as well. Azure Database for MySQL now supports MySQL 8.0 Not a huge update but still a nice feature.

Cloud Data 81

Cloud Data Data Science Azure Natural Language Processing 81

IBM Journey to AI blog

APRIL 9, 2024

This allows the Masters to scale analytics and AI wherever their data resides, through open formats and integration with existing databases and tools. “Hole distances and pin positions vary from round to round and year to year; these factors are important as we stage the data.”

AI 84

AI AI Machine Learning Machine Learning 84

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Lakes Data Warehouse Business Intelligence 52

Data Science 101

NOVEMBER 7, 2019

Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Synapse allows one to use SQL to query petabytes of data, both relational and non-relational, with amazing speed. Azure Synapse. I think this announcement will have a very large and immediate impact.

Data Science 106

Data Science Azure SQL Machine Learning 106

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

Pickl AI

APRIL 6, 2023

Sound knowledge of relational databases or NoSQL databases like Cassandra. An example of the Azure Data Engineer Jobs in India can be evaluated as follows: 6-8 years of experience in the IT sector. Data Warehousing concepts and knowledge should be strong. Knowledge in using Azure Data Factory Volume.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

Smart Data Collective

OCTOBER 27, 2021

Accessing data stored on Google BigQuery is secured with default and customer-managed encryption keys, and you can easily share any business intelligence insight derived from such data with teams and members of your organization with a few clicks. Druid is a real-time analytics database from Apache.

Analytics 113

Analytics Analytics Data Warehouse Business Intelligence 113

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 Engineer Data Engineering Data Engineering 52