Data Science Blog: Data Engineering

DECEMBER 23, 2022

Das Profil des Data Engineers: Big Data High-Tech Auch wenn Data Engineering von Hochschulen und Fortbildungsanbietern gerade noch etwas stiefmütterlich behandelt werden, werden der Einsatz und das daraus resultierende Anforderungsprofil eines Data Engineers am Markt recht eindeutig skizziert.

Data Engineering 52

Data Engineering Data Engineer Engineering Business Intelligence 52

Rockset

MAY 24, 2021

The most popular data storage layers for a serverless stack include: Amazon S3: Amazon Simple Storage Service is offered through AWS as a scalable infrastructure solution. Azure Data Lake: Microsoft's analytics platform and serverless data lake is offered through the company's public cloud, Azure.

Database 40

Database Google Cloud AWS Cloud Storage 40

Data Engineering Podcast

FEBRUARY 18, 2024

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Data lakes are notoriously complex. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!

Data Lake 262

Data Lake High Quality Data Data Warehouse Google Cloud 262

Knowledge Hut

JUNE 26, 2023

The incoming data would be analogous to an event that occurred when a person listened to music, navigated around the website, or authenticated themselves. The processing of the data would take place in real-time, and it would be saved to the data lake at regular intervals (every two minutes).

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Cloudera

MARCH 31, 2021

CDP Public Cloud is now available on Google Cloud. The addition of support for Google Cloud enables Cloudera to deliver on its promise to offer its enterprise data platform at a global scale. CDP Public Cloud is already available on Amazon Web Services and Microsoft Azure. Virtual Machines .

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Google Cloud Cloud Amazon Web Services Cloud Storage 97

Monte Carlo

AUGUST 25, 2023

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

ProjectPro

JANUARY 24, 2023

Since its public release in 2011, BigQuery has been marketed as a unique analytics cloud data warehouse tool that requires no virtual machines or hardware resources. BigQuery is a highly scalable data warehouse platform with a built-in query engine offered by Google Cloud Platform. What is Google BigQuery Used for?

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Knowledge Hut

JULY 6, 2023

It is a built-in Massively parallel processing (MPP) data lake house to handle all your infrastructure observability and security needs. It is a free standalone application that makes working with Azure Storage data on Windows, macOS, and Linux effortlessly.

Amazon Web Services 52

Amazon Web Services Data Lake Java SQL 52

ProjectPro

AUGUST 24, 2021

Source Code: Event Data Analysis using AWS ELK Stack 5) Data Ingestion This project involves data ingestion and processing pipeline with real-time streaming and batch loads on the Google cloud platform (GCP). Create a service account on GCP and download Google Cloud SDK(Software developer kit).

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

AltexSoft

MAY 12, 2023

Unstructured data , on the other hand, is unpredictable and has no fixed schema, making it more challenging to analyze. Without a fixed schema, the data can vary in structure and organization. There are several widely used unstructured data storage solutions such as data lakes (e.g., Hadoop, Apache Spark).

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Unstructured Data NoSQL Hadoop Data Lake 59

Confluent

OCTOBER 16, 2019

Along with using Postgres (or KSQL as shown above) for analytics, the data can be streamed using Kafka Connect into S3, from where it can serve multiple roles. In S3, it can be seen as the “cold storage”, or the data lake, against which as-yet-unknown applications and processes may be run.

Kafka 19

Kafka Building Data Coding 19

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Google Cloud 64

Monte Carlo

AUGUST 19, 2023

With companies moving their data platforms to the cloud, the emergence of cloud-native solutions ( data warehouse vs data lake or even a data lakehouse ) have taken over the market, offering more accessible and affordable options for storing data relative to many on-premises solutions.

Building 52

Building BI Data Lake Data Governance 52