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  5. Amazon AppFlow vs Apache Flink

Amazon AppFlow vs Apache Flink

OverviewDecisionsComparisonAlternatives

Overview

Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K
Amazon AppFlow
Amazon AppFlow
Stacks9
Followers42
Votes0

Amazon AppFlow vs Apache Flink: What are the differences?

## Key Differences between Amazon AppFlow and Apache Flink

Amazon AppFlow is a fully managed integration service that allows customers to securely transfer data between Software-as-a-Service (SaaS) applications and AWS services. On the other hand, Apache Flink is an open-source stream processing framework that enables high-throughput, low-latency data processing pipelines.

1. **Data Sources**: Amazon AppFlow supports a wide range of data sources including Salesforce, Google Analytics, and Slack, while Apache Flink can connect to various data sources such as Apache Kafka, AWS S3, and Apache Cassandra.
2. **Real-time Processing**: Amazon AppFlow primarily focuses on transferring data between different systems in real-time, whereas Apache Flink is designed for real-time processing and analytics of streaming data.
3. **Deployment Options**: Amazon AppFlow is a managed service provided by AWS, offering easy deployment and scalability, while Apache Flink requires users to set up and manage their own infrastructure for deployment.
4. **Data Transformations**: Amazon AppFlow mainly focuses on data transfer and mapping between systems with limited data transformation capabilities, while Apache Flink provides advanced data processing features such as windowing, aggregations, and complex event processing.
5. **Community Support**: Apache Flink has a strong open-source community contributing to its development and providing support, whereas Amazon AppFlow being a managed service does not have the same level of community involvement.
6. **Use Cases**: Amazon AppFlow is best suited for easily transferring data between various applications and services, while Apache Flink is more suitable for complex stream processing tasks requiring real-time analytics and data transformations.

In Summary, the key differences between Amazon AppFlow and Apache Flink lie in their data sources, real-time processing capabilities, deployment options, data transformations, community support, and use cases. Each service offers unique strengths catering to different data processing needs.

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Advice on Apache Flink, Amazon AppFlow

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
Amazon AppFlow
Amazon AppFlow

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 fully managed integration service that enables you to securely transfer data between Software-as-a-Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks. With AppFlow, you can run data flows at nearly any scale at the frequency you choose - on a schedule, in response to a business event, or on demand. You can configure data transformation capabilities like filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps. AppFlow automatically encrypts data in motion, and allows users to restrict data from flowing over the public Internet for SaaS applications that are integrated with AWS PrivateLink, reducing exposure to security threats.

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
Point and click user interface; Native SaaS integrations; Enterprise grade data transformations; High scale data transfer; Data privacy defaults through PrivateLink; Custom encryption keys; IAM policy enforcement; Flexible data flow triggers; Easy to use field mapping; Built in reliability
Statistics
GitHub Stars
25.4K
GitHub Stars
-
GitHub Forks
13.7K
GitHub Forks
-
Stacks
534
Stacks
9
Followers
879
Followers
42
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
Google Analytics
Google Analytics
Slack
Slack
Dynatrace
Dynatrace
Datadog
Datadog
Zendesk
Zendesk
Marketo
Marketo
Snowflake
Snowflake
Amplitude
Amplitude
Veeva
Veeva

What are some alternatives to Apache Flink, Amazon AppFlow?

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.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

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