Apache Spark vs scikit-learn

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

scikit-learn

Stacks1.3K

Followers1.1K

Votes45

GitHub Stars63.9K

Forks26.4K

Apache Spark vs scikit-learn: What are the differences?

Key Differences between Apache Spark and scikit-learn

Apache Spark and scikit-learn are both popular frameworks in the field of data science and machine learning. While they have some similarities, they also have some key differences that set them apart. Here are the key differences between Apache Spark and scikit-learn:

Data Processing: Apache Spark is designed to handle big data processing tasks efficiently by distributing the data across a cluster of machines. It can process data in memory or on disk, making it suitable for large-scale data sets. On the other hand, scikit-learn is designed for smaller data sets that can fit into memory. It operates on a single machine and is not optimized for distributed computing.
Machine Learning Algorithms: Apache Spark provides a comprehensive set of machine learning algorithms that can handle large-scale datasets. It includes algorithms for classification, regression, clustering, recommendation systems, and more. In contrast, scikit-learn offers a wide range of machine learning algorithms as well, but it focuses on traditional machine learning techniques and does not have as many options for big data processing.
Ease of Use: Scikit-learn is known for its simplicity and ease of use. It provides a straightforward API that is easy to understand and use, making it popular among beginners and researchers. Apache Spark, on the other hand, has a steeper learning curve and requires knowledge of distributed computing concepts. It is often used by engineers and data scientists who work with big data.
Scale: One of the major differences between Apache Spark and scikit-learn is their scalability. Apache Spark is designed to scale horizontally by adding more machines to the cluster, allowing it to handle large-scale data processing tasks efficiently. Scikit-learn, on the other hand, is limited by the resources of a single machine and can only handle smaller datasets.
Integration with Big Data Ecosystem: Apache Spark integrates well with other big data technologies such as Hadoop, Hive, and HBase. It can read and write data from and to various data sources, making it a powerful tool for big data analytics. Scikit-learn, on the other hand, is primarily focused on machine learning and does not have built-in support for big data integration.
Community and Ecosystem: Both Apache Spark and scikit-learn have a large and active community of users and developers. However, the ecosystems around these frameworks are quite different. Apache Spark has a rich ecosystem of libraries and tools that extend its functionality, such as Spark SQL, Spark Streaming, and MLlib. Scikit-learn also has a growing ecosystem of libraries, but it is not as extensive as Apache Spark's.

In summary, Apache Spark and scikit-learn differ in terms of data processing capabilities, machine learning algorithms, ease of use, scalability, integration with big data technologies, and the size of their respective ecosystems. Choosing between these frameworks depends on the specific requirements of the project and the size of the dataset.

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Advice on Apache Spark, scikit-learn

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Apache Spark	scikit-learn
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.	scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	-
Statistics
GitHub Stars 42.2K	GitHub Stars 63.9K
GitHub Forks 28.9K	GitHub Forks 26.4K
Stacks 3.1K	Stacks 1.3K
Followers 3.5K	Followers 1.1K
Votes 140	Votes 45
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 26 Scientific computing 19 Easy Cons 2 Limited

What are some alternatives to Apache Spark, scikit-learn?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Related Comparisons

Apache Spark vs scikit-learn: What are the differences?

Key Differences between Apache Spark and scikit-learn

Data Processing: Apache Spark is designed to handle big data processing tasks efficiently by distributing the data across a cluster of machines. It can process data in memory or on disk, making it suitable for large-scale data sets. On the other hand, scikit-learn is designed for smaller data sets that can fit into memory. It operates on a single machine and is not optimized for distributed computing.
Machine Learning Algorithms: Apache Spark provides a comprehensive set of machine learning algorithms that can handle large-scale datasets. It includes algorithms for classification, regression, clustering, recommendation systems, and more. In contrast, scikit-learn offers a wide range of machine learning algorithms as well, but it focuses on traditional machine learning techniques and does not have as many options for big data processing.
Ease of Use: Scikit-learn is known for its simplicity and ease of use. It provides a straightforward API that is easy to understand and use, making it popular among beginners and researchers. Apache Spark, on the other hand, has a steeper learning curve and requires knowledge of distributed computing concepts. It is often used by engineers and data scientists who work with big data.
Scale: One of the major differences between Apache Spark and scikit-learn is their scalability. Apache Spark is designed to scale horizontally by adding more machines to the cluster, allowing it to handle large-scale data processing tasks efficiently. Scikit-learn, on the other hand, is limited by the resources of a single machine and can only handle smaller datasets.
Integration with Big Data Ecosystem: Apache Spark integrates well with other big data technologies such as Hadoop, Hive, and HBase. It can read and write data from and to various data sources, making it a powerful tool for big data analytics. Scikit-learn, on the other hand, is primarily focused on machine learning and does not have built-in support for big data integration.
Community and Ecosystem: Both Apache Spark and scikit-learn have a large and active community of users and developers. However, the ecosystems around these frameworks are quite different. Apache Spark has a rich ecosystem of libraries and tools that extend its functionality, such as Spark SQL, Spark Streaming, and MLlib. Scikit-learn also has a growing ecosystem of libraries, but it is not as extensive as Apache Spark's.

Apache Spark vs scikit-learn

Overview

Apache Spark vs scikit-learn: What are the differences?