Need advice about which tool to choose?Ask the StackShare community!

scikit-learn

1.3K
1.1K
+ 1
44
Apache Spark

3K
3.5K
+ 1
140
Add tool

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:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Advice on scikit-learn and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 512.2K views

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.

See more
Replies (2)
Recommends
on
ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

See more
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 357.7K views
Recommends
on
Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of scikit-learn
Pros of Apache Spark
  • 25
    Scientific computing
  • 19
    Easy
  • 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
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation

Sign up to add or upvote prosMake informed product decisions

Cons of scikit-learn
Cons of Apache Spark
  • 2
    Limited
  • 4
    Speed

Sign up to add or upvote consMake informed product decisions

What is scikit-learn?

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

What is Apache Spark?

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.

Need advice about which tool to choose?Ask the StackShare community!

What companies use scikit-learn?
What companies use Apache Spark?
See which teams inside your own company are using scikit-learn or Apache Spark.
Sign up for StackShare EnterpriseLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with scikit-learn?
What tools integrate with Apache Spark?

Sign up to get full access to all the tool integrationsMake informed product decisions

Blog Posts

Mar 24 2021 at 12:57PM

Pinterest

GitJenkinsKafka+7
3
2128
MySQLKafkaApache Spark+6
2
2000
Aug 28 2019 at 3:10AM

Segment

PythonJavaAmazon S3+16
7
2551
GitHubPythonReact+42
49
40691
What are some alternatives to scikit-learn and Apache Spark?
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.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
XGBoost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
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.
See all alternatives