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Apache Drill VS Apache Spark

Compare Apache Drill VS Apache Spark and see what are their differences

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Apache Drill logo Apache Drill

Schema-Free SQL Query Engine for Hadoop and NoSQL

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • Apache Drill Landing page
    Landing page //
    2023-06-17
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Apache Drill videos

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Using Apache Drill

More videos:

  • Review - Drilling into Data with Apache Drill
  • Review - Apache Drill and the Coolness of Big JSON - Jonathan Janos (MapR)

Apache Spark videos

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Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to Apache Drill and Apache Spark)

Apache Drill

Apache Spark

Databases
11 11%
Databases
89% 89
Relational Databases
100 100%
Relational Databases
0% 0
Big Data
0 0%
Big Data
100% 100
Database Tools
100 100%
Database Tools
0% 0

User comments

Share your experience with using Apache Drill and Apache Spark. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Drill and Apache Spark

Apache Drill Reviews

We have no reviews of Apache Drill yet.
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Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Apache Drill. While we know about 56 links to Apache Spark, we've tracked only 3 mentions of Apache Drill. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Drill mentions (3)

  • Git Query Language (GQL) Aggregation Functions, Groups, Alias
    Also are you familiar with apache drill . The idea is to put an SQL interpreter in front of any kind of database just like you are doing for git here. Source: 10 months ago
  • Roapi: An API Server for Static Datasets
    Looks super interesting and potentially useful. Curious how it compares with Apache Drill (https://drill.apache.org/). - Source: Hacker News / over 2 years ago
  • Does Java have an open source package that can execute SQL on txt/csv?
    Check out Apache Drill: https://drill.apache.org/. Source: over 2 years ago

Apache Spark mentions (56)

  • Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy
    Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉. - Source: dev.to / 2 months ago
  • 🦿🛴Smarcity garbage reporting automation w/ ollama
    Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system. - Source: dev.to / 3 months ago
  • Go concurrency simplified. Part 4: Post office as a data pipeline
    Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 5 months ago
  • Five Apache projects you probably didn't know about
    Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago
  • Spark – A micro framework for creating web applications in Kotlin and Java
    A JVM based framework named "Spark", when https://spark.apache.org exists? - Source: Hacker News / 11 months ago
View more

What are some alternatives?

When comparing Apache Drill and Apache Spark, you can also consider the following products

Apache Calcite - Relational Databases

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

DataGrip - Tool for SQL and databases

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

Hadoop - Open-source software for reliable, scalable, distributed computing

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