ProjectPro
OCTOBER 5, 2018
With market leaders like Microsoft and SAP expanding their horizons at the end user industry, HaaS is likely to witness rapid growth in the next 7 years.Organizations like Commerzbank have already launched new platforms based on HaaS solutions which demonstrate that HaaS is a promising solution for building and managing big data clusters.
ProjectPro
JANUARY 19, 2022
Good knowledge of various machine learning and deep learning algorithms will be a bonus. Knowledge of popular big data tools like Apache Spark, Apache Hadoop, etc. Good communication skills as a data engineer directly works with the different teams.
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Knowledge Hut
NOVEMBER 2, 2023
Data Aggregation Working with a sample of big data allows you to investigate real-time data processing, big data project design, and data flow. Learn how to aggregate real-time data using several big data tools like Kafka, Zookeeper, Spark, HBase, and Hadoop.
ProjectPro
APRIL 20, 2017
You have read some of the best Hadoop books , taken online hadoop training and done thorough research on Hadoop developer job responsibilities – and at long last, you are all set to get real-life work experience as a Hadoop Developer.
Knowledge Hut
JUNE 23, 2023
Data engineers make a tangible difference with their presence in top-notch industries, especially in assisting data scientists in machine learning and deep learning. You should be well-versed in Python and R, which are beneficial in various data-related operations. What is Data Modeling? What is HDFS?
AltexSoft
FEBRUARY 11, 2023
It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);
Knowledge Hut
JUNE 20, 2023
Skills A data engineer should have good programming and analytical skills with big data knowledge. A machine learning engineer should know deep learning, scaling on the cloud, working with APIs, etc. Examples Pull daily tweets from the data warehouse hive spreading in multiple clusters.
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