Apache Spark vs Superhuman

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Superhuman

Stacks60

Followers60

Votes0

Apache Spark vs Superhuman: What are the differences?

Introduction

Apache Spark and Superhuman are two different technologies used in different domains. Apache Spark is an open-source distributed computing system used for big data processing and analytics, while Superhuman is an email client designed for enhanced productivity and workflow management. Here are the key differences between Apache Spark and Superhuman:

Purpose: Apache Spark is mainly used for distributed data processing and analytics tasks, such as data cleansing, data transformation, and machine learning. On the other hand, Superhuman is a specialized email client that focuses on providing a seamless email experience with features like lightning-fast search, advanced keyboard shortcuts, and email tracking.
Domain: Apache Spark is widely used in the big data industry for processing large-scale datasets and real-time streaming data. It caters to the needs of developers, data scientists, and analysts working on big data projects. Superhuman, on the other hand, is primarily used by individuals who deal with a high volume of emails, such as executives, salespeople, and entrepreneurs.
Architecture: Apache Spark follows a distributed computing model and utilizes a cluster of machines to process data in parallel. It provides fault-tolerance and scalability, making it suitable for handling large datasets. Superhuman, on the other hand, is a desktop application that focuses on optimizing the user interface and user experience for email management. It leverages local resources and network connectivity to deliver a smooth email workflow.
Features: Apache Spark offers a rich set of features for distributed data processing, including data streaming, SQL queries, machine learning libraries, and graph processing. It enables users to perform complex data operations and build sophisticated analytics pipelines. Superhuman, on the other hand, offers features like snooze, email scheduling, read receipts, and contact insights. It aims to enhance the productivity and efficiency of email management.
Community and Ecosystem: Apache Spark has a large and vibrant open-source community, providing support, updates, and contributions to the platform. It has a wide range of integrations with various big data tools and frameworks. Superhuman, on the other hand, is a proprietary software with a smaller community base. It offers limited integrations with other applications and focuses more on providing a curated email experience.
Deployment and Scalability: Apache Spark can be deployed on various platforms, including standalone clusters, Hadoop clusters, and cloud environments. It can scale horizontally by adding more nodes to the cluster to handle larger datasets and higher workloads. Superhuman, being a desktop application, relies on the resources of the local machine. It may not have the same level of scalability as Apache Spark in terms of data processing capabilities.

In summary, Apache Spark is a distributed computing platform for big data processing and analytics, while Superhuman is an optimized email client aiming to enhance productivity and workflow management. They differ in terms of purpose, domain, architecture, features, community support, and scalability capabilities.

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Advice on Apache Spark, Superhuman

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	Superhuman
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 not just another email client. They are rebuilding the inbox from the ground up to make you brilliant at what you do. Specifically designed it for those of you who want the best.
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	A.I. triage; Undo send; Insights from social networks; Follow-up reminders; Scheduled messages; Read statuses
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 60
Followers 3.5K	Followers 60
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	Zoho Mail Nicereply Chatwork Zapier Nimble GoToMeeting Teamweek

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Mailchimp

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Amazon Athena

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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.

Gmail

An easy to use email app that saves you time and keeps your messages safe. Get your messages instantly via push notifications, read and respond online & offline, and find any message quickly.

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.

Campaign Monitor

Campaign Monitor makes it easy to attract new subscribers, send them beautiful email newsletters and see stunning reports on the results.

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

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Related Comparisons

Apache Spark vs Superhuman: What are the differences?

Introduction

Purpose: Apache Spark is mainly used for distributed data processing and analytics tasks, such as data cleansing, data transformation, and machine learning. On the other hand, Superhuman is a specialized email client that focuses on providing a seamless email experience with features like lightning-fast search, advanced keyboard shortcuts, and email tracking.
Domain: Apache Spark is widely used in the big data industry for processing large-scale datasets and real-time streaming data. It caters to the needs of developers, data scientists, and analysts working on big data projects. Superhuman, on the other hand, is primarily used by individuals who deal with a high volume of emails, such as executives, salespeople, and entrepreneurs.
Architecture: Apache Spark follows a distributed computing model and utilizes a cluster of machines to process data in parallel. It provides fault-tolerance and scalability, making it suitable for handling large datasets. Superhuman, on the other hand, is a desktop application that focuses on optimizing the user interface and user experience for email management. It leverages local resources and network connectivity to deliver a smooth email workflow.
Features: Apache Spark offers a rich set of features for distributed data processing, including data streaming, SQL queries, machine learning libraries, and graph processing. It enables users to perform complex data operations and build sophisticated analytics pipelines. Superhuman, on the other hand, offers features like snooze, email scheduling, read receipts, and contact insights. It aims to enhance the productivity and efficiency of email management.
Community and Ecosystem: Apache Spark has a large and vibrant open-source community, providing support, updates, and contributions to the platform. It has a wide range of integrations with various big data tools and frameworks. Superhuman, on the other hand, is a proprietary software with a smaller community base. It offers limited integrations with other applications and focuses more on providing a curated email experience.
Deployment and Scalability: Apache Spark can be deployed on various platforms, including standalone clusters, Hadoop clusters, and cloud environments. It can scale horizontally by adding more nodes to the cluster to handle larger datasets and higher workloads. Superhuman, being a desktop application, relies on the resources of the local machine. It may not have the same level of scalability as Apache Spark in terms of data processing capabilities.

Apache Spark vs Superhuman

Overview

Apache Spark vs Superhuman: What are the differences?