Data Engineering Podcast

MAY 27, 2018

It’s easy to get one started but difficult to manage as new requirements are added and greater scalability becomes necessary.

Data Pipeline 100

Data Pipeline MongoDB Google Cloud Scala 100

Monte Carlo

OCTOBER 31, 2024

In addition, AI data engineers should be familiar with programming languages such as Python , Java, Scala, and more for data pipeline, data lineage, and AI model development. Get familiar with data warehouses, data lakes, and data lakehouses, including MongoDB , Cassandra, BigQuery, Redshift and more.

Data Engineer 52

Data Engineer Data Engineering Engineering Unstructured Data 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 processing: Data engineers should know data processing frameworks like Apache Spark, Hadoop, or Kafka, which help process and analyze data at scale.

Data Engineer 52

Data Engineer Data Engineering Engineering Google Cloud 52

Knowledge Hut

JUNE 20, 2023

Languages Python, SQL, Java, Scala R, C++, Java Script, and Python Tools Kafka, Tableau, Snowflake, etc. Kafka: Kafka is a top engineering tool highly valued by big data experts. Machine learning engineer: A machine learning engineer is an engineer who uses programming languages like Python, Java, Scala, etc.

Machine Learning 52

Machine Learning Data Engineer Data Engineering Engineering 52

Ascend.io

SEPTEMBER 14, 2023

Read More: Data Automation Engineer: Skills, Workflow, and Business Impact Python for Data Engineering Versus SQL, Java, and Scala When diving into the domain of data engineering, understanding the strengths and weaknesses of your chosen programming language is essential.

Data Engineer 52

Data Engineer Data Engineering Python Engineering 52

Knowledge Hut

SEPTEMBER 6, 2023

Programming Languages : Good command on programming languages like Python, Java, or Scala is important as it enables you to handle data and derive insights from it. Big Data Frameworks : Familiarity with popular Big Data frameworks such as Hadoop, Apache Spark, Apache Flink, or Kafka are the tools used for data processing.

Big Data 52

Big Data Certification Hadoop Kafka 52

Cloud Academy

JANUARY 27, 2022

Some good options are Python (because of its flexibility and being able to handle many data types), as well as Java, Scala, and Go. Microsoft SQL Server Document-oriented database: MongoDB (classified as NoSQL) The Basics of Data Management, Data Manipulation and Data Modeling This learning path focuses on common data formats and interfaces.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

Knowledge Hut

NOVEMBER 17, 2023

Programming and Scripting Skills Building data processing pipelines requires knowledge of and experience with coding in programming languages like Python, Scala, or Java. Therefore, it is essential to have a thorough understanding of programming languages like Python, Java, or Scala.

Data Engineer 52

Data Engineer Data Engineering Engineering Scala 52

Knowledge Hut

SEPTEMBER 26, 2023

We should also be familiar with programming languages like Python, SQL, and Scala as well as big data technologies like HDFS , Spark, and Hive. Programming languages like Python, Java, or Scala require a solid understanding of data engineers. Learn about well-known ETL tools such as Xplenty, Stitch, Alooma, etc.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

AltexSoft

JULY 29, 2022

Streaming analytics became possible with the introduction of Apache Kafka , Apache Spark , Apache Storm , Apache Flink , and other tools to build real-time data pipelines. Two other most-wanted Big Data instruments — Apache Kafka and Apache Spark — belong to the same ecosystem. Python and R are essential for data analysts; and.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Edureka

FEBRUARY 9, 2023

Data engineers must be well-versed in programming languages such as Python, Java, and Scala. A data engineer should be familiar with popular Big Data tools and technologies such as Hadoop, MongoDB, and Kafka. The most common data storage methods are relational and non-relational databases.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Mining 52

Knowledge Hut

DECEMBER 26, 2023

They use technologies like Storm or Spark, HDFS, MapReduce, Query Tools like Pig, Hive, and Impala, and NoSQL Databases like MongoDB, Cassandra, and HBase. They also make use of ETL tools, messaging systems like Kafka, and Big Data Tool kits such as SparkML and Mahout.

Data Science 97

Data Science BI Business Intelligence Machine Learning 97

Knowledge Hut

JUNE 23, 2023

Other Competencies You should have proficiency in coding languages like SQL, NoSQL, Python, Java, R, and Scala. Equip yourself with the experience and know-how of Hadoop, Spark, and Kafka, and get some hands-on experience in AWS data engineer skills, Azure, or Google Cloud Platform. You can also post your work on your LinkedIn profile.

Data Engineer 52

Data Engineer Data Engineering Engineering Non-relational Database 52

Confluent

SEPTEMBER 20, 2019

As a distributed system for collecting, storing, and processing data at scale, Apache Kafka ® comes with its own deployment complexities. To simplify all of this, different providers have emerged to offer Apache Kafka as a managed service. BigQuery, Amazon Redshift, and MongoDB Atlas) and caches (e.g.,

Kafka 21

Kafka Management Cloud AWS 21

ProjectPro

JANUARY 19, 2022

Data engineers must thoroughly understand programming languages such as Python, Java, or Scala. Hadoop, MongoDB, and Kafka are popular Big Data tools and technologies a data engineer needs to be familiar with. Relational and non-relational databases are among the most common data storage methods.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Storage 52

Databand.ai

DECEMBER 13, 2022

He currently runs a YouTube channel, E-Learning Bridge , focused on video tutorials for aspiring data professionals and regularly shares advice on data engineering, developer life, careers, motivations, and interviewing on LinkedIn.

Data Engineer 52

Data Engineer Data Engineering Engineering AWS 52

ProjectPro

NOVEMBER 14, 2015

It plays a key role in streaming in the form of Spark Streaming libraries, interactive analytics in the form of SparkSQL and also provides libraries for machine learning that can be imported using Python or Scala. It is an improvement over Hadoop’s two-stage MapReduce paradigm.

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

JUNE 17, 2016

To ensure that big data recruiters find you for the right Hadoop job, focus on highlighting the specific Hadoop skills, spark skills or data science skills you want to work with, such as Pig & Hive , HBase, Oozie and Zookeeper, Apache Spark, Scala, machine learning , python, R language, etc.

Hadoop 40

Hadoop Recruitment Big Data NoSQL 40

ProjectPro

JULY 27, 2021

E.g. Redis, MongoDB, Cassandra, HBase , Neo4j, CouchDB What is data modeling? Prepare for Your Next Big Data Job Interview with Kafka Interview Questions and Answers How is a data warehouse different from an operational database? What is a case class in Scala? E.g. PostgreSQL, MySQL, Oracle, Microsoft SQL Server.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40