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Amazon Athena vs Apache Flink vs Apache Spark: What are the differences?
# Key Differences Between Amazon Athena and Apache Flink and Apache Spark
<Write Introduction here>
1. **Data Processing Model**: Amazon Athena is more suitable for ad-hoc querying of data stored in Amazon S3, while Apache Flink and Apache Spark are used for complex stream processing and batch processing tasks, respectively.
2. **Deployment**: Amazon Athena is a fully managed service, which means users don't have to provision or manage any infrastructure, while Apache Flink and Apache Spark require setting up and managing clusters for deployment.
3. **Real-time Processing**: Apache Flink is known for its low-latency and high-throughput real-time stream processing capabilities, making it a popular choice for real-time data processing tasks, while Apache Spark focuses more on batch processing, although it also supports real-time processing.
4. **Programming Language Support**: Apache Flink supports Java, Scala, and Python, while Apache Spark provides support for Java, Scala, Python, and R. In contrast, Amazon Athena primarily uses SQL for querying data.
5. **Data Storage Support**: Amazon Athena is tightly integrated with Amazon S3 for data storage, making it easy to query data directly from S3, whereas Apache Flink and Apache Spark have more flexibility in terms of data sources and can work with various storage systems.
6. **Cost**: Amazon Athena follows a pay-per-query pricing model, which can be cost-effective for ad-hoc querying, while Apache Flink and Apache Spark require upfront infrastructure investment for cluster setup, which may result in higher costs for long-running processing tasks.
In Summary, Amazon Athena is ideal for ad-hoc querying on Amazon S3, Apache Flink excels in real-time stream processing, and Apache Spark is well-suited for batch processing tasks.
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Learn MorePros of Amazon Athena
Pros of Apache Flink
Pros of Apache Spark
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 Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
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 Flink
Cons of Apache Spark
Cons of Amazon Athena
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Cons of Apache Flink
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Cons of Apache Spark
- Speed4
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What is 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.
What is 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.
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.
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What are some alternatives to Amazon Athena, Apache Flink, and Apache Spark?
Presto
Distributed SQL Query Engine for Big Data
Amazon Redshift Spectrum
With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
Amazon Redshift
It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
Spectrum
The community platform for the future.