Amazon AppFlow vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Amazon AppFlow

Stacks9

Followers42

Votes0

Amazon AppFlow vs Apache Spark: What are the differences?

Introduction:

1. Scalability and Performance: Amazon AppFlow is a fully managed integration service that enables secure exchange of data between AWS services and SaaS applications without writing custom code, while Apache Spark is an open-source distributed computing system that provides high-level APIs in languages like Java, Scala, Python, and R. Amazon AppFlow is more suitable for simpler integration tasks with predefined connectors and transformations, whereas Apache Spark is designed for complex data processing tasks requiring scalability and high performance.

2. Use Cases: Amazon AppFlow is ideal for businesses looking to quickly set up data flows between various sources and destinations with minimal effort. On the other hand, Apache Spark is well-suited for organizations dealing with massive amounts of data that need to be processed efficiently in a distributed computing environment. The use cases for Apache Spark range from data analytics and machine learning to real-time processing and stream processing.

3. Cost Management: Amazon AppFlow follows a pay-as-you-go pricing model based on the volume of data processed, the number of flow runs, and other factors. In contrast, Apache Spark is open-source software that can be deployed on cloud platforms or on-premises clusters, allowing more control over infrastructure costs. Organizations using Apache Spark can optimize their resources based on workload demands to better manage costs.

4. Learning Curve and Development Effort: Amazon AppFlow offers a low-code approach to data integration, requiring minimal setup and configuration. In comparison, Apache Spark requires developers to have a strong understanding of distributed computing concepts and programming languages like Scala or Python. The learning curve for Apache Spark can be steeper, but it provides more flexibility and control over data processing workflows.

5. Real-time Data Processing: Amazon AppFlow excels in enabling data synchronization and batch processing tasks between different systems in near real-time. In contrast, Apache Spark provides robust real-time processing capabilities through its streaming APIs, allowing for continuous computation on live data streams. Organizations with stringent real-time data processing requirements may prefer the advanced capabilities offered by Apache Spark.

6. Ecosystem Integration: Amazon AppFlow seamlessly integrates with various AWS services, SaaS applications, and databases to facilitate data exchange and automation. Apache Spark, on the other hand, has a rich ecosystem of libraries and connectors for different data sources, machine learning frameworks, and streaming platforms. The extensibility and versatility of Apache Spark make it a preferred choice for organizations with diverse data processing and analysis needs.

In Summary, Amazon AppFlow and Apache Spark differ in terms of scalability, use cases, cost management, learning curve, real-time data processing, and ecosystem integration, catering to different requirements in data integration and processing workflows.

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Advice on Apache Spark, Amazon AppFlow

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.

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Detailed Comparison

Apache Spark	Amazon AppFlow
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.	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.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	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 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 9
Followers 3.5K	Followers 42
Votes 140	Votes 0
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	No community feedback yet
Integrations
No integrations available	Google Analytics Slack Dynatrace Datadog Zendesk Marketo Snowflake Amplitude Veeva

What are some alternatives to Apache Spark, Amazon AppFlow?

Presto

Distributed SQL Query Engine for Big Data

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.

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.

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.

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

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

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

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

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.

Related Comparisons

Amazon AppFlow vs Apache Spark: What are the differences?

Introduction:

Amazon AppFlow vs Apache Spark

Overview