data engineer interview questions github

This exam is meant to demonstrate your knowledge of databases, data processing, as well as your programming skills in the language of your choice.

The pairing phase, which should be viewed as a cooperative effort, is meant to give us an idea of what it will be like to work together.

We have included example data and programme code. The example schema generates a straightforward table and includes sample code for loading data from a CSV file and writing it to a JSON file in a number of popular programming languages. To use the examples, launch the database, and use the Docker containers, follow the instructions at the bottom of this document.

There are a number of steps that we need you to take. We anticipate this will only take a few hours of your time.

To demonstrate how to handle a straightforward data ingest and output, we have provided an example schema and code.

Below are instructions on how to use the example schema and code as well as run and connect to the database.

Some questions we discuss directly on the live stream.

All Interview Questions
  • What are windowing functions?
  • What is a stored procedure?
  • Why would you use them?
  • What are atomic attributes?
  • Explain ACID props of a database.
  • How to optimize queries?
  • What are the different types of JOIN (CROSS, INNER, OUTER)?

Top 10+ Data Engineer Interview Questions and Answers

What are the main features of Apache Spark?

Main features of Apache Spark are as follows:

  • Performance: The key feature of Apache Spark is its Performance. With Apache Spark we can run programs up to 100 times faster than Hadoop MapReduce in memory. On disk we can run it 10 times faster than Hadoop.
  • Ease of Use: Spark supports Java, Python, R, Scala etc. languages. So it makes it much easier to develop applications for Apache Spark.
  • Integrated Solution: In Spark we can create an integrated solution that combines the power of SQL, Streaming and data analytics. R+ un Everywhere: Apache Spark can run on many platforms. It can run on Hadoop, Mesos, in Cloud or standalone. It can also connect to many data sources like HDFS, Cassandra, HBase, S3 etc.
  • Stream Processing: Apache Spark also supports real time stream processing. With real time streaming we can provide real time analytics solutions. This is very useful for real-time data.
  • What is a Resilient Distribution Dataset in Apache Spark?

    Apache Spark’s Resilient Distribution Dataset (RDD) is an abstraction of data. It is a resilient distributed collection of records split among several partitions. RDD hides the data partitioning and distribution behind the scenes. Main features of RDD are as follows:

  • Distributed: Data in a RDD is distributed across multiple nodes.
  • Resilient: RDD is a fault- tolerant dataset. In case of node failure, Spark can re- compute data.
  • Dataset: It is a collection of data similar to collections in Scala.
  • Immutable: Data in RDD cannot be modified after creation. But we can transform it using a Transformation.
  • For updates, join our slack workspace and follow me on LinkedIn (dkisler).

    How will you improve the performance of a program in Hive?

    A Hive program can be made to perform better in a variety of ways. Some of these include the following: Data Structure: When writing a Hive program, we must choose the appropriate data structure for our needs. Standard Library: We should use standard library methods whenever possible. Standard library methods perform significantly better than user implementation. Abstraction: Occasionally, excessive abstraction and indirection can make a program run slowly. We should remove the redundant abstraction in code. Algorithm: Using the proper algorithm can significantly alter a program. In order to solve our problem with high performance, we must locate and choose the best algorithm.

    FAQ

    What questions are asked in Data Engineer interview?

    60+ Data Engineer Interview Questions and Answers in 2022
    • 1) Explain Data Engineering. …
    • 2) What is Data Modelling? …
    • 3) List various types of design schemas in Data Modelling.
    • 4) Distinguish between structured and unstructured data. …
    • 5) Explain all components of a Hadoop application. …
    • 6) What is NameNode?

    How can I pass data engineer interview?

    How to Prepare for a Data Engineer Interview
    1. Create a Stellar Data Engineer Resume. …
    2. Practice Coding. …
    3. Brush Up on Data Engineering Fundamentals. …
    4. SQL. …
    5. Data Structure and Algorithms. …
    6. System Design. …
    7. Python. …
    8. Take Mock Interviews to Prepare for Behavioral Interview Rounds.

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