ODSC - Open Data Science

SEPTEMBER 27, 2023

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. Before we address the questions, ‘ What is data version control ?’

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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. Organisationen können je nach ihren spezifischen Bedürfnissen und Anforderungen zwischen einem Data Warehouse und einem Data Lakehouse wählen.

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Data Warehouse Data Lakes Azure AWS 147

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. Hadoop systems and data lakes are frequently mentioned together.

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Data Lakes Data Warehouse Hadoop Machine Learning 221

phData

MARCH 7, 2023

The task of keeping multiple databases in sync so that data is accurate, up-to-date, and highly available is every data consumer’s biggest challenge. Oracle is one of the largest IT companies whose flagship product, Oracle Database, is a relational database management system. What is Oracle?

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Hadoop Database Data Warehouse AWS 59

Smart Data Collective

APRIL 8, 2020

Dating back to the 1970s, the data warehousing market emerged when computer scientist Bill Inmon first coined the term ‘data warehouse’. Created as on-premise servers, the early data warehouses were built to perform on just a gigabyte scale.

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Data Warehouse Big Data Big Data Business Intelligence 134

Data Science Connect

JANUARY 27, 2023

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. Understand data warehousing concepts: Data warehousing is the process of collecting, storing, and managing large amounts of data.

Data Engineering 130

Data Engineering Data Engineer Data Engineering Data Engineering 130

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 Engineer Data Engineering Data Engineering 52

phData

AUGUST 9, 2023

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. This is unlike the more traditional ETL method, where data is transformed before loading into the data warehouse.

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ETL Data Warehouse Cloud Data Big Data 52

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 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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Pickl AI

APRIL 6, 2023

Consequently, here is an overview of the essential requirements that you need to have to get a job as an Azure Data Engineer. In-depth knowledge of distributed systems like Hadoop and Spart, along with computing platforms like Azure and AWS. Sound knowledge of relational databases or NoSQL databases like Cassandra.

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Azure Data Engineering Data Engineer Data Engineering 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

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Data Science Analytics Analytics Data Scientist 86

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.

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ML ML Data Lakes Machine Learning 52

Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements.

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Mlearning.ai

MAY 16, 2023

It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database. First, the data is extracted from the various sources and brought into a staging area.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

phData

FEBRUARY 5, 2024

Creating the databases, schemas, roles, and access grants that comprise a data system information architecture can be time-consuming and error-prone. Luckily phData has created a template-driven Provision Tool that automates onboarding users and projects to Snowflake, allowing your data teams to start producing real value immediately.

SQL 52

SQL Database Data Quality Data Warehouse 52

IBM Journey to AI blog

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. It will automatically scale queries to handle any size data set, so you can focus on analyzing your data.

SQL 52

SQL Database Apache Hadoop Data Science 52

Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Data Engineering 91

Data Engineering Data Engineer Data Engineering Data Engineering 91

Alation

AUGUST 4, 2022

Without the right skillsets, no value can be created from data. New Big Data Concepts vs Cloud Delivered Databases? So, what has the emergence of cloud databases done to change big data? For starters, the cloud has made data more affordable. “Setting up Hadoop on-premises was a huge undertaking.

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Big Data Big Data Apache Kafka Data Lakes 95

IBM Journey to AI blog

SEPTEMBER 25, 2023

Because of its distributed nature, Presto scales for petabytes and exabytes of data. The evolution of Presto at Uber Beginning of a data analytics journey Uber began their analytical journey with a traditional analytical database platform at the core of their analytics. It also provides features like indexing and caching.”

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