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Apache Spark Alternatives
Similar projects and alternatives to Apache Spark
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Trino
Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
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Airflow
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
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Pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
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Apache Arrow
Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
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Onboard AI
Learn any GitHub repo in 59 seconds. Onboard AI learns any GitHub repo in minutes and lets you chat with it to locate functionality, understand different parts, and generate new code. Use it for free at www.getonboard.dev.
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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.
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Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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Redis
Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.
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redpanda
Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!
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delta
An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs (by delta-io)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better Apache Spark alternative or higher similarity.
Apache Spark reviews and mentions
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 2023-08-12.
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Integrate Pyspark Structured Streaming with confluent-kafka
Apache Spark - https://spark.apache.org/
- Rest in Peas: The Unrecognized Death of Speech Recognition (2010)
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Gotta write this on my resume
So for example contributing to say spark may better for experience(and resume) than Twitter-the algorithm.
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Query Real Time Data in Kafka Using SQL
Additionally, one of the challenges of working with Kafka is how to efficiently analyze and extract insights from the large volumes of data stored in Kafka topics. Traditional batch processing approaches, such as Hadoop MapReduce or Apache Spark, can be slow and expensive, and may not be suitable for real-time analytics. To address this challenge, you can use SQL queries with Kafka to analyze and extract insights from the data in real time.
- Unveiling the Analytics Industry in Bangalore
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Apache Iceberg as storage for on-premise data store (cluster)
Spark for your transformation compute engine. Get Spark to talk to Nessie.
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5 Best Practices For Data Integration To Boost ROI And Efficiency
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka.
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Forward Compatible Enum Values in API with Java Jackson
We’re not discussing the technical details behind the deduplication process. It could be Apache Flink, Apache Spark, or Kafka Streams. Anyway, it’s out of the scope of this article.
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Uber Interview Experience/Asking Suggestions
One place to look are the projects repo's and docs, once you have a good idea of how the system is architected poking around pieces of the codebase can be helpful in letting you really understand their internals. I personally enjoy going through spark repo and trino repo and the documentation for both projects is decent and can answer many of your questions.
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DataOps 101: An Introduction to the Essential Approach of Data Management Operations and Observability
DataOps is a collaborative effort within an organization, with many different teams of people working together to ensure that DataOps functions properly and delivers data value [3]. So, before the data is delivered to end users, it is subjected to a number of treatments and refinements from multiple teams. Data scientists first use their data science techniques, such as machine learning and deep learning to build models using software stacks such as Python or R and tools such as Spark or Tensorflow, among others, and the models are then transferred to data engineers, who collect and manage the data used to train and evaluate these models, while data developers and data architects create complete applications that include the models. The data governance team then implements data access controls for training and benchmarking purposes, while the operations team ( "Ops") is in charge of putting everything together and making it available to end users.
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A note from our sponsor - Onboard AI
getonboard.dev | 1 Dec 2023
Stats
Basic Apache Spark repo stats
95
37,245
9.9
7 days ago
apache/spark is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of Apache Spark is Scala.
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
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