Rockset

MAY 17, 2022

Limitations of NoSQL SQL supports complex queries because it is a very expressive, mature language. And when systems such as Hadoop and Hive arrived, it married complex queries with big data for the first time. That changed when NoSQL databases such as key-value and document stores came on the scene.

SQL 52

SQL NoSQL Hadoop MongoDB 52

Knowledge Hut

APRIL 25, 2023

Strong programming skills: Data engineers should have a good grasp of programming languages like Python, Java, or Scala, which are commonly used in data engineering. Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

ProjectPro

JANUARY 19, 2022

Data Engineering Requirements Here is a list of skills needed to become a data engineer: Highly skilled at graduation-level mathematics. Good skills in computer programming languages like R, Python, Java, C++, etc. High efficiency in advanced probability and statistics.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Rockset

NOVEMBER 12, 2020

Offloading analytics from MongoDB establishes clear isolation between write-intensive and read-intensive operations. In most scenarios, MongoDB can be used as the primary data storage for write-only operations and as support for quick data ingestion. Monstache is also available as a sync daemon and a container.

MongoDB 40

MongoDB NoSQL Data Pipeline Data Storage 40

AltexSoft

JULY 29, 2022

Apache Hadoop is an open-source Java-based framework that relies on parallel processing and distributed storage for analyzing massive datasets. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. MongoDB: an NoSQL database with additional features.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

OCTOBER 28, 2015

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment. Sqoop makes data analysis efficient.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Processing: This is the final step in deploying a big data model.

Big Data 52

Big Data Hadoop AWS Relational Database 52