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Apache Flink vs Google BigQuery: What are the differences?
Introduction
Apache Flink and Google BigQuery are both powerful tools used in data processing and analysis. While they serve similar purposes, there are several key differences between the two.
Cost Model: Apache Flink is an open-source stream processing framework that can be used for free, while Google BigQuery is a cloud-based data warehouse service that charges users based on the amount of data processed and the computational resources used.
Data Storage: Apache Flink does not provide built-in data storage capabilities, requiring users to integrate it with external storage systems like Apache Kafka or Apache Hadoop. On the other hand, Google BigQuery is a fully-managed data warehouse that provides scalable storage and processing capabilities built on Google Cloud Storage.
Data Processing: Apache Flink is designed for real-time stream processing and batch processing, offering powerful windowing and event-time processing features. Google BigQuery, on the other hand, is primarily focused on fast, ad-hoc queries on massive datasets using SQL-like syntax.
Scale and Performance: Apache Flink is designed to handle high-throughput and low-latency data streams, enabling near real-time processing. Google BigQuery, on the other hand, is built for scalable, high-performance analytics on large datasets, prioritizing ease of use and query performance.
Ecosystem and Integrations: Apache Flink has a rich ecosystem and supports integration with various data sources and sinks, offering flexibility in data ingestion and output. Google BigQuery integrates closely with other Google Cloud services, making it seamless to build end-to-end data pipelines and analytics workflows.
Management and Administration: Apache Flink requires manual management and configuration of deployment environments, making it more suitable for experienced users who need granular control. Google BigQuery, being a fully managed service, handles infrastructure provisioning, maintenance, and security, making it more accessible and user-friendly for less technical users.
In summary, Apache Flink is an open-source stream processing framework suitable for real-time processing, while Google BigQuery is a fully managed cloud-based data warehouse optimized for scalable analytics on large datasets. The choice between the two depends on specific requirements, budget considerations, and the level of control and management needed.
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"
Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.
Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.
BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.
BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.
Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.
BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.
We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution
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 Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
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Cons of Apache Flink
Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0
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