Software Alternatives & Reviews

Apache Spark VS Cloud Dataprep

Compare Apache Spark VS Cloud Dataprep and see what are their differences

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.

Cloud Dataprep logo Cloud Dataprep

Cloud Dataprep by Trifacta is a data prep & cleansing service for exploring, cleaning & preparing datasets using a simple drag & drop browser environment
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Cloud Dataprep Landing page
    Landing page //
    2023-09-18

Apache Spark videos

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

Cloud Dataprep videos

Cloud Dataprep Tutorial - Getting Started 101

More videos:

  • Review - Advanced Data Cleanup Techniques using Cloud Dataprep (Cloud Next '19)
  • Demo - Google Cloud Dataprep Premium product demo

Category Popularity

0-100% (relative to Apache Spark and Cloud Dataprep)
Databases
100 100%
0% 0
Office & Productivity
0 0%
100% 100
Big Data
100 100%
0% 0
Development
0 0%
100% 100

User comments

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Reviews

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

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...
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...
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...

Cloud Dataprep Reviews

We have no reviews of Cloud Dataprep yet.
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Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Cloud Dataprep. While we know about 56 links to Apache Spark, we've tracked only 3 mentions of Cloud Dataprep. 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 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 / about 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 / 4 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 / 4 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

Cloud Dataprep mentions (3)

  • How to upload large excel-sheet file (90MB) to BigQuery
    Check Google Cloud Dataprep – requires no coding, you can normalize & clean up the data as well. I've done this many times, saved me headaches from dirty data in Excel files. Source: almost 2 years ago
  • Data mapping process
    Not sure if I understand the request but a commercial tool I know of is https://cloud.google.com/dataprep - it sounds like that could be helpful but I am not sure. Source: over 2 years ago
  • The beginner GCP user seeking some help from the experts.
    If you need to adjust the underlying data, you can use Cloud Dataprep to do manipulations (here). Source: about 3 years ago

What are some alternatives?

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

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

Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

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

GeoSpock - GeoSpock is the platform for data lake management, providing a unified view of the data assets within an organization and making it easily accessible.

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

Delta Lake - Application and Data, Data Stores, and Big Data Tools