Spark project ideas combine programming, machine learning, and big data tools in a complete architecture. It is a relevant tool to master for beginners who are looking to break into the world of fast analytics and computing technologies.
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Apache Spark is a top choice among programmers when it comes to big data processing. This open-source framework provides a unified interface for programming entire clusters. Its built-in modules provide extensive support for SQL, machine learning, stream processing, and graph computation. Also, it can process data in parallel and recover the loss itself in case of failures.
Spark is neither a programming language nor a database. It is a general-purpose computing engine built on Scala. It is easy to learn Spark if you have a foundational knowledge of Python and other APIs, including Java and R.
The Spark ecosystem has a wide range of applications due to the advanced processing capabilities it possesses. We have listed a few use cases below to help you move forward in your learning journey!
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Spark Project Ideas & Topics
1. Spark Job Server
This project helps in handling Spark job contexts with a RESTful interface, allowing submission of jobs from any language or environment. It is suitable for all aspects of job and context management.
The development repository with unit tests and deploy scripts. The software is also available as a Docker Container that prepackages Spark with the job server.
2. Apache Mesos
The AMPLab at UC Berkeley developed this cluster manager to enable fault-tolerant and flexible distributed systems to operate effectively. Mesos abstracts computer resources like memory, storage, and CPU away from the physical and virtual machines.
It is an excellent tool to run any distributed application requiring clusters. From bigwigs like Twitter to companies like Airbnb, a variety of businesses use Mesos to administer their big data infrastructures. Here are some of its key advantages:
- It can handle workloads using dynamic load sharing and isolation
- It parks itself between the application layer and the OS to enable efficient deployment in large-scale environments
- It facilitates numerous services to share the server pool
- It clubs the various physical resources into a unified virtual resource
You can duplicate this open-source project to understand its architecture, which comprises a Mesos Master, an Agent, and a Framework, among other components.
3. Spark-Cassandra Connector
Cassandra is a scalable NoSQL data management system. You can connect Spark with Cassandra using a simple tool. The project will teach you the following things:
- Writing Spark RDDs and DataFrames to Apache Cassandra tables
- Executing CQL queries in your Spark application
Earlier, you had to enable interaction between Spark and Cassandra via extensive configurations. But with this actively-developed software, you can connect the two without the previous requirement. You can find the use case freely available on GitHub.
4. Predicting flight delays
You can use Spark to perform practical statistical analysis (descriptive as well as inferential) over an airline dataset. An extensive dataset analysis project can familiarize you with Spark MLib, its data structures, and machine learning algorithms.
Furthermore, you can take up the task of designing an end-to-end application for forecasting delays in flights. You can learn the following things through this hands-on exercise:
- Installing Apache Kylin and implementing star schema
- Executing multidimensional analysis on a large flight dataset using Spark or MapReduce
- Building Cubes using RESTful API
- Applying Cubes using the Spark engine
5. Data pipeline based on messaging
A data pipeline involves a set of actions from the time of data ingestion until the processes of extraction, transformation, or loading take place. By simulating a batch data pipeline, you can learn how to make design decisions along the way, build the file pipeline utility, and learn how to test and troubleshoot the same. You can also gather knowledge about constructing generic tables and events in Spark and interpreting output generated by the architecture.
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6. Data consolidation
This is a beginner project on creating a data lake or an enterprise data hub. No considerable integration effort is required to consolidate data under this model. You can merely request group access and apply MapReduce and other algorithms to start your data crunching project.
Such data lakes are especially useful in corporate setups where data is stored across different functional areas. Typically, they materialize as files on Hive tables or HDFS, offering the benefit of horizontal scalability.
It is an incubation project within the Apache Foundation that brings Jupyter-style notebooks to Spark. Its IPython interpreter offers developers a better way to share and collaborate on designs. Zeppelin supports a range of other programming languages besides Python. The list includes Scala, SparkSQL, Hive, shell, and markdown.
With Zeppelin, you can perform the following tasks with ease:
- Use a web-based notebook packed with interactive data analytics
- Directly publish code execution results (as an embedded iframe) to your website or blog
- Create impressive, data-driven documents, organize them, and team-up with others
8. E-commerce project
Spark has gained prominence in data engineering functions of e-commerce environments. It is capable of aiding the design of high-performing data infrastructures. Let us first look at what all you is possible in this space:
- Streaming of real-time transactions through clustering algorithms, such as k-means
- Scalable collaborative filtering with Spark MLib
- Combining results with unstructured data sources (for example, product reviews and comments)
- Adjusting recommendations with changing trends
The dynamicity of does not end here. You can use the interface to address specific challenges in your e-retail business. Try your hand at a unique big data warehouse application that optimizes prices and inventory allocation depending upon geography and sales data. Through this project, you can grasp how to approach real-world problems and impact the bottom line.
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Alluxio acts as an in-memory orchestration layer between Spark and storage systems like HDFS, Amazon S3, Ceph, etc. On the whole, it moves data from a central warehouse to the computation framework for processing. The research project was initially named Tachyon when it was developed at the University of California.
Apart from bridging the gap, this open-source project improves analytics performance when working with big data and AI/ML workloads in any cloud. It provides dedicated data-sharing capabilities across cluster jobs written in Apache Spark, MapReduce, and Flink. You can call it a memory-centric virtual distributed storage system.
10. Streaming analytics project on fraud detection
Streaming analytics applications are popular in the finance and security industry. It makes sense to analyze transactional data while the process is underway, instead of finding out about frauds at the end of the cycle. Spark can help build such intrusion and anomaly detection tools with HBase as the general data store. You can spot another instance of this kind of tracking in inventory management systems.
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11. Complex event processing
Through this project, you can explore applications with ultra-low latency where sub-seconds, picoseconds, and nanoseconds are involved. We have mentioned a few examples below.
- High-end trading applications
- Systems for a real-time rating of call records
- Processing IoT events
The speedy, lambda architecture of Spark allows millisecond response time for these programs.
Apart from the topics mentioned above, you can also look at many other Spark project ideas. Let’s say you want to make a near real-time vehicle-monitoring application. Here, the sensor data is simulated and received using Spark Streaming and Flume. The Redis data structure can serve as a pub/sub middleware in this Spark project.
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12. The use case for gaming
The video game industry requires reliable programs for immediate processing and pattern discovery. In-game events demand quick responses and efficient capabilities for player retention, auto-adjustment of complexity levels, target advertising, etc. In such scenarios, Apache Spark can attend to the variety, velocity, and volume of the incoming data.
Several technology powerhouses and internet companies are known to use Spark for analyzing big data and managing their ML systems. Some of these top-notch names include Microsoft, IBM, Amazon, Yahoo, Netflix, Oracle, and Cisco. With the right skills, you can pursue a lucrative career as a full-stack software developer, data engineer, or even work in consultancy and other technical leadership roles.
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The above list on Spark project ideas is nowhere near exhaustive. So, keep unravelling the beauty of the codebase and discovering new applications!
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What are the disadvantages of using Apache Spark?
Apache Spark is widely used by industries because it is open-source, robust, and fast. Moreover, it is built to execute fast computation in industries. However, there are certain drawbacks and challenges that developers come across. To begin, Apache Spark needs code optimisation manually since it lacks so through automation. This is a problem when other platforms shift towards automation. The second challenge is the constant issue with small files. Apache Spark with Hadoop adds small file issues for developers. The next one is Apache Spark’s incompatibility to fit well in a multi-user environment which restricts it from working with multiple users at once.
What are some of the ideal projects that use Apache Spark?
Apache Spark has extended massively as it is simple and powerful. There are multiple useful situations of Spark, such as it reduces learning time, i.e., you can learn languages like Python and SQL with a low learning curve which can result in building multiple projects from scratch. Apache Spark also extends to Big Data as it incorporates technologies such as Azure and AWS. Setting up Apache Spark with data-lake technologies lets you decouple storage. Another ideal project is where Apache Spark can be used in batch and streaming tasks. If you are working on a project that needs batch processing along with real-time processing, you can use Apache Spark and its inbuilt libraries instead of individual Big Data tools.
When should Apache Spark not be used at all?
There are certain cases when Apache Spark’s specialisation fails. In such situations, using a Big Data engine can be extremely beneficial instead of Spark. Considering Apache Kafka over Spark could be a great choice when you have several sources and destinations that constantly move data quickly and rapidly. Spark can be later used to transfer the data from Kafka. Its cluster memory where default processing continuously takes place means that you might have to consider alternatives while dealing with virtual machines that have less computing capacity.