Need advice about which tool to choose?Ask the StackShare community!
Apache Spark vs AtScale: What are the differences?
Introduction:
Key differences between Apache Spark and AtScale are as follows:
Architecture: Apache Spark is a distributed computing system that operates as a processing engine for large-scale data processing, while AtScale is an analytics platform that enables companies to leverage their existing data infrastructure to provide a unified view for business intelligence. Apache Spark is designed for data analytics and machine learning, offering in-memory processing and fault tolerance, while AtScale focuses on providing a virtual data warehouse layer on top of existing data sources.
Use Case: Apache Spark is commonly used for data processing, machine learning, and real-time stream processing tasks, ideal for data scientists and engineers. On the other hand, AtScale is tailored for business users, enabling them to access and analyze data in a self-service manner without the need for complex data wrangling or SQL knowledge, making it more suitable for business intelligence and analytics teams.
Scalability: Apache Spark is highly scalable and can handle massive datasets by distributing computation across multiple nodes, making it suitable for big data processing. AtScale, on the other hand, does not offer the same level of scalability as Apache Spark in terms of processing huge volumes of data, as its focus is more on providing a unified view of data sources for analysis rather than parallel processing of large datasets.
Integration: Apache Spark integrates well with various data sources and tools, such as Hadoop, Kafka, and SQL databases, allowing for seamless data ingestion and processing. AtScale, on the other hand, focuses on providing a layer of abstraction for data sources, allowing users to access data from different platforms without the need for integration or transformation, simplifying the data access and analysis process.
Cost: Apache Spark is an open-source project, offering a cost-effective solution for organizations looking to process and analyze large volumes of data without the need for expensive proprietary software licenses. AtScale, however, is a commercial product with licensing fees, catering more to enterprise users looking for advanced analytics capabilities and support services.
Performance Optimization: Apache Spark provides advanced performance optimizations through features like caching, lazy evaluation, and in-memory processing, ensuring efficient data processing and computation. AtScale focuses more on providing a unified semantic layer for business users, optimizing query performance by translating user queries into efficient queries for the data sources underneath, ensuring fast and reliable data access for analysis.
In Summary, Apache Spark and AtScale differ in architecture, use case, scalability, integration, cost, and performance optimization, catering to different user needs in data processing and analytics.
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.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- 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:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- 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
- When the Timer fires, read the 1st record from the State and send out as the output record.
- 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.
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"
Pros of AtScale
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
Sign up to add or upvote prosMake informed product decisions
Cons of AtScale
Cons of Apache Spark
- Speed4
Sign up to add or upvote consMake informed product decisions
What is AtScale?
What is Apache Spark?
Need advice about which tool to choose?Ask the StackShare community!
What companies use AtScale?
What companies use Apache Spark?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with AtScale?
What tools integrate with Apache Spark?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+4
+7+8
+4
+11
+6
+16
+37