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Amazon Athena vs Apache Spark: What are the differences?
Amazon Athena and Apache Spark are two popular data processing tools. Let's discuss the key differences between them.
Data processing model: Amazon Athena is a query service that enables users to analyze data in Amazon S3 using standard SQL queries. It is serverless and doesn't require any infrastructure setup or management. On the other hand, Apache Spark is a distributed processing framework that allows for the parallel processing of big datasets across a cluster of computers. It provides a wide range of APIs for data processing, including batch, interactive, and real-time analytics.
Scalability and Performance: With Amazon Athena, the performance scales automatically based on the query complexity and data size, as it leverages the underlying power of Amazon S3 and Presto engine. However, when dealing with large datasets or complex workflows, Apache Spark provides better scalability as it can distribute the workload across multiple nodes in a cluster, resulting in faster processing times.
Data Sources: Amazon Athena primarily works with data stored in Amazon S3, allowing users to perform queries directly on files in CSV, JSON, Parquet, or other formats. In contrast, Apache Spark has a more extensive range of data source connectors, enabling it to interact with various data storage systems like Hadoop Distributed File System (HDFS), HBase, Cassandra, and more.
Computational Model: Amazon Athena is a serverless, on-demand service where users are only billed based on the queries executed and the amount of data scanned. It automatically takes care of query execution, maintaining metadata, and scaling resources. In contrast, Apache Spark requires users to set up dedicated clusters, manage resources, and deploy applications. Spark also offers the flexibility to perform complex data manipulations and transformations using its Resilient Distributed Dataset (RDD) abstraction.
Real-Time Processing: While both Amazon Athena and Apache Spark can handle batch processing, Apache Spark has a specific focus on real-time processing. Spark provides various streaming APIs (such as Structured Streaming) that enable near-real-time data processing and analytics. This capability makes Apache Spark suitable for use cases requiring low-latency data processing and real-time analytics.
Ecosystem and Integration: Apache Spark has a vast ecosystem with support for various machine learning libraries (MLlib), graph processing (GraphX), and stream processing (Spark Streaming). It seamlessly integrates with other popular big data tools like Apache Hadoop, Apache Hive, and Apache Kafka. In comparison, Amazon Athena offers a more focused ecosystem around querying data in Amazon S3, with limited direct integrations.
In summary, Amazon Athena is a serverless, query-based service specifically designed for analyzing data stored in Amazon S3, offering easy setup and scalability. On the other hand, Apache Spark is a distributed processing framework that allows for parallel data processing, provides a wider range of data source connectors, and offers more extensive options for real-time processing and integration with various big data tools.
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"
Hi all,
Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?
you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster
First of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. If performance is not so critical and queries will be predictable somewhat I'd go for Athena.
Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. In both cases you may need to adapt the data model to fit your queries better. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain.
I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.
It depend of the nature of your data (structured or not?) and of course your queries (ad-hoc or predictible?). For example you can look at partitioning and columnar format to maximize MPP capabilities for both Athena and Redshift
you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved
Pros of Amazon Athena
- Use SQL to analyze CSV files16
- Glue crawlers gives easy Data catalogue8
- Cheap7
- Query all my data without running servers 24x76
- No data base servers yay4
- Easy integration with QuickSight3
- Query and analyse CSV,parquet,json files in sql2
- Also glue and athena use same data catalog2
- No configuration required1
- Ad hoc checks on data made easy0
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
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Cons of Amazon Athena
Cons of Apache Spark
- Speed4
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