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  1. Stackups
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  5. Apache Flink vs BDS

Apache Flink vs BDS

OverviewDecisionsComparisonAlternatives

Overview

Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K
BDS
BDS
Stacks3
Followers9
Votes0
GitHub Stars996
Forks61

Apache Flink vs BDS: What are the differences?

# Apache Flink vs. BDS

Apache Flink and BDS are two popular big data processing frameworks with key differences that distinguish them in different aspects.

1. **Architecture**: Apache Flink is a stream-processing engine that supports both batch and stream processing, while BDS is a data processing service built on Apache Spark that is predominantly used for batch processing.
2. **Optimization Techniques**: Apache Flink employs advanced optimization techniques such as pipelining and dynamic optimization, enabling efficient memory management and performance enhancements. In contrast, BDS focuses more on compatibility with Spark, leveraging its optimization techniques for batch processing.
3. **Programming Language Support**: Apache Flink offers support for multiple languages such as Java, Scala, and Python, providing flexibility for developers. On the other hand, BDS primarily supports Scala and Java, limiting language options for development.
4. **Connectivity**: Apache Flink provides native connectors for various data sources and sinks, facilitating seamless integration with different systems. BDS relies on Spark's connector ecosystem for data source and sink connectivity, which may require additional customization for specific integrations.
5. **State Management**: Apache Flink offers robust state management capabilities, with support for large-scale state handling and fault tolerance mechanisms. In contrast, BDS lacks advanced state management features, limiting its capabilities in handling complex stateful processing requirements.
6. **Community Support**: Apache Flink has a vibrant open-source community that actively contributes to its development and provides widespread support. Meanwhile, BDS is part of the Microsoft Azure ecosystem, benefiting from its integration with other Azure services but may have a more specialized support network.

In Summary, Apache Flink and BDS differ in their architecture, optimization techniques, programming language support, connectivity, state management, and community support, catering to distinct use cases and requirements in the big data processing domain.

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Advice on Apache Flink, BDS

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

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

Detailed Comparison

Apache Flink
Apache Flink
BDS
BDS

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.

It is a realtime data aggregating, analyzing and visualization service for chain-like unstructured data from all kinds of 3rd party Blockchains.

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
Cover dozens of well-known Blockchain projects, including BTC, ETH, LTC, XRP, BCH, etc;Provide an interactive data visualization BI tool;Support standard SQL Query statements so that complex query logics can be implemented easily;Provide products that query data on the Blockchain in China and it also provides data visualization BI tools
Statistics
GitHub Stars
25.4K
GitHub Stars
996
GitHub Forks
13.7K
GitHub Forks
61
Stacks
534
Stacks
3
Followers
879
Followers
9
Votes
38
Votes
0
Pros & Cons
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
No community feedback yet
Integrations
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka
Grafana
Grafana
PostgreSQL
PostgreSQL
Kafka
Kafka
Microsoft SQL Server
Microsoft SQL Server
Confluent
Confluent

What are some alternatives to Apache Flink, BDS?

Apache Spark

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Ethereum

Ethereum

A decentralized platform for applications that run exactly as programmed without any chance of fraud, censorship or third-party interference.

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