Knowledge Hut

JUNE 23, 2023

Let us look at the steps to becoming a data engineer: Step 1 - Skills for Data Engineer to be Mastered for Project Management Learn the fundamentals of coding skills, database design, and cloud computing to start your career in data engineering. Pathway 2: How to Become a Certified Data Engineer?

Data Engineering 52

Data Engineering Data Engineer Engineering Non-relational Database 52

Knowledge Hut

MARCH 26, 2024

Cloud Solutions Architect Role Overview: Design and implement cloud-based solutions leveraging platforms like AWS, Azure, or Google Cloud to meet business objectives. The Cloud Computing course syllabus covers most aspects of this field in detail.

Amazon Web Services 52

Amazon Web Services Google Cloud Computer Science AWS 52

Knowledge Hut

MARCH 20, 2024

Full-Stack Engineer Front-end and back-end database design are the domains of expertise for full-stack engineers and developers. Together with designing the end-user interface and the complex systems and databases that operate it, they can work independently to design, create, and develop a whole working web application.

Software Engineer 52

Software Engineer Software Engineering Engineering Java 52

AltexSoft

JULY 29, 2022

On top of HDFS, the Hadoop ecosystem provides HBase , a NoSQL database designed to host large tables, with billions of rows and millions of columns. It lets you run MapReduce and Spark jobs on data kept in Google Cloud Storage (instead of HDFS); or. MongoDB: an NoSQL database with additional features.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Edureka

JANUARY 12, 2024

Platforms like AWS Lambda, Azure Functions, and Google Cloud Functions are popular choices. This table summarizes the key differences between normalization and denormalization in database design, each with its own advantages and trade-offs depending on the specific requirements and context of the database application.

NoSQL 52

NoSQL Java MongoDB Programming 52

Rockset

APRIL 15, 2022

Most were cloud native ( Amazon Kinesis , Google Cloud Dataflow) or were commercially adapted for the cloud ( Kafka ⇒ Confluent, Spark ⇒ Databricks). They were unaffordable for most companies. Then a new generation of event-streaming platforms emerged. Many (Kafka, Spark and Flink) were open source.

Analytics Application 52

Analytics Application Data Warehouse Raw Data Kafka 52