Name: Apache Spark Streaming
Rating: 8.7 (7 reviews)
Author: Apache

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Users have made several recommendations for Apache Spark based on their experiences. 

First, users recommend using Apache Spark for big data processing. They believe it is a good choice for handling large databases and provides strong analytical capabilities. Users highly recommend Apache Spark, especially for large businesses that require real-time data processing.

Second, users suggest understanding the problem at hand and choosing the right tool from the toolkit, rather than forcing Apache Spark. While Apache Spark is a powerful tool, it requires prior expertise and may not always be the most appropriate solution. Users emphasize the importance of considering other tools before deciding to use Apache Spark.

Third, users highlight the well-built API extension in Apache Spark for those already familiar with the tool. This feature allows users to easily extend and customize their workflows, making their job easier and more enjoyable. Users recommend taking advantage of this functionality and exploring its capabilities.

Overall, users recommend trying out Apache Spark and leveraging its strengths in big data processing and analytics. However, they also advise carefully assessing the problem and selecting the most suitable tool from the toolkit rather than blindly adopting Apache Spark.

Difficult to learn and understand: Some users have found Spark Streaming challenging to learn and understand, mentioning a lack of available resources for learning. They feel that a strong background in big data, along with knowledge of MapReduce and Java, is required to fully grasp the concepts. The abundance of documentation can also be overwhelming for newcomers.

Complexity and resource-intensive: Several users have expressed that Spark Streaming jobs are complex and resource-intensive. They require engineers who know how to tune the job properly to avoid excessive resource consumption. Debugging and troubleshooting pipelines can be difficult, while installation and configuration processes are complex as well.

Compatibility issues with other platforms: Users have mentioned compatibility issues between Spark Streaming and other data platforms. Not all versions of Spark Streaming are compatible with all types of data sources, which limits its usability in certain environments.

Valuable tool for data streaming: Users find Spark Streaming to be a valuable tool for easily streaming data, particularly after using Kafka. It is especially useful for handling moderate amounts of data.

In-depth understanding of big data processing: Users appreciate that Spark Streaming provides comprehensive knowledge of big data and is easy to implement by configuring existing parameters.

Seamless integration with Apache Spark ecosystem: Reviewers commend the seamless integration of Spark Streaming with the Apache Spark ecosystem, granting access to a wide range of libraries and tools. They also find the programming model user-friendly, while the fault tolerance mechanisms ensure dependable data processing even in the face of failures.

Spark Streaming has proven to be a valuable tool for a variety of use cases based on user experiences. Users have successfully utilized Spark Streaming to transfer data continuously from a source, creating pipelines for their personal projects. This feature has been effective in capturing real-time data and solving various business problems, such as collecting data from UI and saving it in a desired format and database.

Customers have found value in using Spark Streaming to gather real-time insights and enable faster decision-making, proactive monitoring, and quick response to changing conditions or events. The scalability, fault tolerance, and integration capabilities of Spark Streaming have enhanced its effectiveness in handling large volumes of streaming data, leading to improved operational efficiency and better business outcomes. Users have leveraged Spark Streaming for various data processing tasks including data copy, collection, cleansing, gathering from multiple sources, and analysis to create meaningful metrics for better customer service.

Moreover, Spark Streaming has been implemented for real-time parallel processing of data from telecom networks, including decoding, analysis, analytics, post-processing, and storage. Users have also utilized machine learning models with the help of Spark Streaming. The open-source nature of the product has been beneficial in extending the mapreduce model and providing tremendous assistance to users. Additionally, Spark Streaming has been used for event processing and analysis, reading leads from Kafka, segregating valuable data for analytics in a Datalake ecosystem.

Furthermore, users have found Spark Streaming ideal for live data streaming and big data processing due to its ease of maintenance and usability. It has been valuable in processing logs from cloud applications enabling near real-time data processing in micro batches. Users have utilized Spark Streaming to create personalized experiences and integrate various platforms for multimedia spreading. Additionally, it has been effective in analyzing live streaming data for real-time analytics.

Overall, users have experienced increased efficiency with the utilization of Spark Streaming across different industries such as RPA companies and schools where it facilitated mass communication. It has also proved instrumental in aggregating IoT events in near real-time and streaming real-time data for predictive analytics and control of air conditioning units. In conclusion, Spark Streaming, with its diverse capabilities, has been widely used to address a variety of user needs in real-time data processing, analytics, and machine learning.

Amazon Kinesis is a streaming analytics suite for data intake from video or other disparate sources and applying analytics for machine learning (ML) and business intelligence.

Amazon Kinesis Data Streams

Amazon Kinesis Data Firehose

Amazon Kinesis Data Analytics

Amazon Kinesis Video Streams

Confluent Cloud is a cloud-native service for Apache Kafka used to connect and process data in real time with a fully managed data streaming platform. Confluent Platform is the self-managed version.

Basic

Standard

Apache Spark Streaming

Streaming Analytics is performing analytic computations on data streaming from devices, sensors, websites, social media, applications, infrastructure systems, and more. This category of tools is an evolution of Complex Event Processing (CEP) software, designed specifically for the big data era.

Streaming Analytics

Likelihood to Recommend

Real-Time Data Analysis

Visualization Dashboards

Data Ingestion from Multiple Data Sources

Low Latency

Integrated Development Tools

Data wrangling and preparation

Linear Scale-Out

Machine Learning Automation

Data Enrichment

The Apache Software Foundation provides support for the Apache community of open-source software projects. The Apache projects are characterized by a collaborative, consensus based development process, an open and pragmatic software license, and a desire to create high quality software that leads the way in its field.

We consider ourselves not simply a group of projects sharing a server, but rather a community of developers and users.

Streaming Analytics is performing analytic computations on streaming data. Data streams can come from devices, sensors, websites, social media, applications, infrastructure systems, and more.

Easy-to-understand pictorial illustration of data with graphs charts and dashboards

Ability to ingest data from many sources including Internet of Things (IoT) endpoint data, stock trading data etc, as well as static data

How many milli-seconds or seconds it takes to ingest, analyze and respond to an incoming event or
data-point

Tools to allow developers to rapidly create streaming applications via a graphical user interface and selection of predefined
functions and operators

Tools to rapidly prepare data for analysis by normalization, data cleansing, etc.

Easy to scale out or scale down by visually changing the resource allocation. This allows changes in load or traffic to be handled without interruptions

Machine learning helps automate predictive scoring on streaming data

Ability to enrich the data stream with static reference data

Presto

Copper Project

Copper

REVE Antivirus

Belkin Meeting Room Power Center

Business Manager

Apache Spark Streaming 2021-07-01 10:58:48

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TrustRadius

Complex tool

Large data

Home

Streaming Analytics Software

Apache Spark Streaming is a great tool and is being utilized in our company to do batch processing which is its specialization. It is user-friendly. Using it we can even process our massive data at a very fast speed and it uses complicated algorithms. It has enabled us in saving time.

It is amazing in solving complicated transformative logic.
It is straightforward to program.
It is a very quick tool.
It processes large data within a fraction of seconds.

There must be more documentation.
It is a profoundly complex tool.
Its in-memory processing consumes massive memory.

Cost and time-effective tool for our business.
We can integrate with Jupyter with many conveniences.
Its high-speed data processing has proved beneficial for us.

REVE Antivirus, Copper Project, Belkin Meeting Room Power Center

Apache Spark Streaming

Overview

What is Apache Spark Streaming?