opaque-sql VS Apache Spark

Compare opaque-sql vs Apache Spark and see what are their differences.


An encrypted data analytics platform (by mc2-project)

Apache Spark

Apache Spark - A unified analytics engine for large-scale data processing (by apache)
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opaque-sql Apache Spark
2 22
151 31,120
3.3% 1.5%
8.3 10.0
30 days ago 1 day ago
Scala Scala
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of opaque-sql. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-08-10.
  • How to Run Spark SQL on Encrypted Data
    dev.to | 2021-08-10
    Introducing Opaque SQL, an open-source platform for securely running Spark SQL queries on encrypted data. Built by top systems and security researchers at UC Berkeley, the platform uses hardware enclaves to securely execute queries on private data in an untrusted environment.
  • Announcing MC²: Securely perform analytics and machine learning on confidential data
    dev.to | 2021-06-17
    The MC2 Compute Services: MC2 offers several compute services: these include Spark SQL, distributed XGBoost, and secure aggregation for federated learning. All are intended to run in a primarily untrusted environment, such as a cluster of machines hosted on a public cloud, that has support for trusted execution environments (hardware enclaves). Data is encrypted in transit using a client key and only ever decrypted inside hardware enclaves, providing the previously mentioned security guarantees for data-in-use. For all compute services, MC2 leverages the Open Enclave SDK, a project intended to provide a consistent API for a variety of different enclave architectures.

Apache Spark

Posts with mentions or reviews of Apache Spark. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-17.
  • What is B2D Sector?
    dev.to | 2021-10-17
    Example tools:\ Tensorflow, Tableau, Apache Spark, Matlab, Jupyter
  • Why should I invest in raptoreum? What makes it different
    reddit.com/r/raptoreum | 2021-09-25
    For your first question, if you are interested I encourage you to read the smart contracts paper here: https://docs.raptoreum.com/_media/Raptoreum_Contracts_EN.pdf and then to dig into what Apache Spark can do here: https://spark.apache.org/
  • How to use Spark and Pandas to prepare big data
    dev.to | 2021-09-21
    Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce).
  • Google Colab, Pyspark, Cassandra remote cluster combine these all together
    dev.to | 2021-09-13
  • How to Run Spark SQL on Encrypted Data
    dev.to | 2021-08-10
    For those of you who are new, Apache Spark is a popular distributed computing framework used by data scientists and engineers for processing large batches of data. One of its modules, Spark SQL, allows users to interact with structured, tabular data. This can be done through a DataSet/DataFrame API available in Scala or Python, or by using standard SQL queries. Here you can see a quick example of both below:
  • Machine Learning Tools and Algorithms
    Apache Spark :- A massive data processing engine with built-in modules for streaming, SQL, Machine Learning (ML), and graph processing, Apache Spark is recognized for being quick, simple to use, and general. It is also known for being fast, simple to use, and generic.
  • Strategies for running multiple Spark jobs simultaneously?
  • Python VS Scala
    reddit.com/r/scala | 2021-07-02
    Actually, it does. Scala has Spark for data science and some ML libs like Smile.
  • Best library for CSV to XML or JSON.
    reddit.com/r/javahelp | 2021-07-01
    Apache Beam may be what you're looking for. It will work with both Python and Java. It's used by GCP in the Cloud Dataflow service as a sort of streaming ETL tool. It occupies a similar niche to Spark, but is a little easier to use IMO.
  • 5 Best Big Data Frameworks You Can Learn in 2021
    dev.to | 2021-06-18
    Both Fortune 500 and small companies are looking for competent people who can derive useful insight from their huge pile of data and that's where Big Data Framework like Apache Hadoop, Apache Spark, Flink, Storm, and Hive can help.

What are some alternatives?

When comparing opaque-sql and Apache Spark you can also consider the following projects:

Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)

Scalding - A Scala API for Cascading

luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

Smile - Statistical Machine Intelligence & Learning Engine


mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services

dpark - Python clone of Spark, a MapReduce alike framework in Python

Scio - A Scala API for Apache Beam and Google Cloud Dataflow.

Summingbird - Streaming MapReduce with Scalding and Storm

Deeplearning4j - Model import deployment framework for retraining models (pytorch, tensorflow,keras) deploying in JVM Micro service environments, mobile devices, iot, and Apache Spark

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Apache Flink - Apache Flink