Overview

Apache Spark

Stacks3.0K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Google Cloud Dataflow

Stacks223

Followers497

Votes19

Apache Spark vs Google Cloud Dataflow: What are the differences?

Introduction

Apache Spark and Google Cloud Dataflow are both popular distributed data processing frameworks used for big data analytics and processing. While they share some similarities, there are several key differences between them that set them apart in terms of their capabilities and functionalities. In this article, we will explore six key differences between Apache Spark and Google Cloud Dataflow.

Programming Language Support: Apache Spark offers support for multiple programming languages such as Scala, Java, Python, and R. This allows developers to choose the language they are most comfortable with for writing Spark applications. On the other hand, Google Cloud Dataflow primarily supports Java and Python for writing data processing pipelines.
Data Processing Model: Apache Spark uses a batch processing model, where data is processed in batches. It also provides support for real-time streaming processing through its Spark Streaming module. In contrast, Google Cloud Dataflow is designed specifically for real-time stream processing, making it well-suited for applications that require low-latency processing and near real-time insights.
Managed Service vs Open-Source: Apache Spark is an open-source framework, which means it can be deployed on various platforms and environments. It gives users more control over the deployment and management of their Spark clusters. On the other hand, Google Cloud Dataflow is a fully managed service offered by Google Cloud Platform. This means that Google takes care of the infrastructure and management of the Dataflow pipeline, allowing users to focus more on building their data processing logic.
Integration with Cloud Services: Being a part of the Google Cloud Platform, Google Cloud Dataflow integrates seamlessly with other Google Cloud services such as BigQuery, Pub/Sub, Datastore, etc. This makes it easy to build end-to-end data pipelines using these services. In comparison, while Apache Spark can also integrate with various cloud services, the level of integration and ease of use may depend on the specific cloud provider and the libraries/driver support available.
Windowing and Triggers: Google Cloud Dataflow provides more advanced windowing and triggering capabilities compared to Apache Spark. It offers flexible windowing options such as fixed windows, sliding windows, and session windows, along with various types of triggers. This allows users to define more complex window-based computations and handle late data more efficiently.
Data Parallelism: Apache Spark uses RDDs (Resilient Distributed Datasets) as the fundamental data structure, which provides a flexible and powerful abstraction for distributed data processing. RDDs allow for efficient data parallelism by splitting the data into partitions and executing computations on them in parallel. In contrast, Google Cloud Dataflow uses the concept of PCollections, which provides similar parallel processing capabilities but with a more unified and simplified programming model.

In summary, Apache Spark offers support for multiple programming languages, supports both batch and real-time processing, and gives users more control over deployment and management. Google Cloud Dataflow is a fully managed service that is designed specifically for real-time stream processing, integrates well with Google Cloud services, and offers more advanced windowing and triggering capabilities.

Advice on Apache Spark, Google Cloud Dataflow

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	Google Cloud Dataflow
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.	Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
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	Fully managed; Combines batch and streaming with a single API; High performance with automatic workload rebalancing Open source SDK;
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.0K	Stacks 223
Followers 3.5K	Followers 497
Votes 140	Votes 19
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	Pros 7 Unified batch and stream processing 5 Autoscaling 4 Fully managed 3 Throughput Transparency

What are some alternatives to Apache Spark, Google Cloud Dataflow?

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.