ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Rockset

DECEMBER 17, 2020

Overview of the Customer 360 App Our app will make use of real-time data on customer orders and events. We’ll use Rockset to get data from different sources and run analytical queries that power our app in Retool. From there, we’ll create a data API for the SQL query we write in Rockset.

Building 40

Building Aggregated Data SQL Data Ingestion 40

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Monte Carlo

FEBRUARY 15, 2023

Second, to reduce your time-to-detection you need to be end-to-end across your entire data system which may include warehouses or lakes from other vendors or other components of the modern data stack. Finally, where and how the data pipeline broke isn’t always obvious. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

ProjectPro

AUGUST 24, 2021

Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines. Google BigQuery receives the structured data from workers.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

JANUARY 25, 2022

Easy Processing- PySpark enables us to process data rapidly, around 100 times quicker in memory and ten times faster on storage. When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems.

Big Data 52

Big Data Data Process Process Kafka 52

ProjectPro

OCTOBER 28, 2015

Getting data into the Hadoop cluster plays a critical role in any big data deployment. Data ingestion is important in any big data project because the volume of data is generally in petabytes or exabytes. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc.,

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52