Apache Flink vs Google BigQuery

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

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.

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Advice on Google BigQuery, Apache Flink

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

576k views576k

Comments

Julien

CTO at Hawk

Sep 19, 2020

Decided

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

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Comments

Detailed Comparison

Google BigQuery	Apache Flink
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.	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.
All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.	Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
Statistics
GitHub Stars -	GitHub Stars 25.4K
GitHub Forks -	GitHub Forks 13.7K
Stacks 1.8K	Stacks 534
Followers 1.5K	Followers 879
Votes 152	Votes 38
Pros & Cons
Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas	Pros 16 Unified batch and stream processing 8 Out-of-the box connector to kinesis,s3,hdfs 8 Easy to use streaming apis 4 Open Source 2 Low latency
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to Google BigQuery, Apache Flink?

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.

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.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Related Comparisons

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.

Apache Flink vs Google BigQuery

Overview

Apache Flink vs Google BigQuery: What are the differences?