Apache Storm vs Spark: Full Comparison & Use Cases

Data is all around us, whether it’s a small startup’s customer list or a giant company’s financial reports. Companies use this information to see how their business is running and where they can improve things. According to a report, global data creation is estimated to grow to over 394 zettabytes by 2028.

With the rise of real-time data, the demand for effective streaming solutions is increasing rapidly. Distributed computing frameworks like Apache Storm and Apache Spark are the leading frameworks in the Big Data area. According to Statista, the global market share of leading data processing technologies in 2023 was Apache Kafka (16.88%), Apache Hadoop (12.87%), and Apache Spark (6.91%). This data highlights the importance of efficient real-time processing frameworks like Storm and Spark.

Storm specializes in real-time stream processing, while Spark offers both batch and micro-batch processing capabilities. Both tools are powerful, but which one is right for you? This blog provides a comprehensive comparison of Apache Storm vs Spark, focusing on their features and use cases.

Apache Storm Overview in 2025

In the era of ever-increasing data streams, Apache Storm remains a vital tool for real-time data processing. It's designed to handle high-velocity data with remarkable speed and reliability. Whether you're monitoring social media trends, detecting fraud in financial transactions, or analyzing sensor data from IoT devices, Storm allows you to gain immediate insights and take timely action. Let's dive into the core aspects that make Apache Storm a key player in the Big Data arena.

Core Architecture & Real-Time Capabilities

Storm’s architecture is built for low-latency, true real-time stream processing, allowing responses in milliseconds. If your application demands immediate action based on incoming data, Storm's architecture has you covered. 

The core components of Storm include:

• Nimbus: Nimbus is the master node in Apache Storm's architecture. It distributes user-submitted code (called topologies) to worker nodes, assigns tasks, and monitors their execution. Nimbus ensures fault tolerance by reassigning tasks if any worker fails. It is similar in function to Hadoop’s JobTracker.
• Supervisors: Supervisors run on each worker node in the cluster. Their role is to manage worker processes by starting and stopping them based on instructions from Nimbus. Each Supervisor ensures the correct tasks are running on its machine and communicates the status back to Nimbus.
• Workers: Workers are the actual processes responsible for data processing. Each worker executes a portion of a topology, running specific spouts and bolts assigned to it. Multiple worker processes can run on a single node, providing scalability and parallelism.
• ZooKeeper: ZooKeeper acts as a coordination service in Storm’s architecture. It maintains synchronization between Nimbus and Supervisors, ensuring consistent cluster state and fault tolerance. ZooKeeper handles leadership election, node registration, and failure recovery.
• Spouts: Spouts serve as the entry point for data into a Storm topology. They connect to external data sources like APIs, message queues, or databases and continuously emit data as streams of tuples. Spouts essentially feed real-time data into the processing pipeline.
• Bolts: Bolts are where the main computation and data transformation happen. They receive tuples from spouts or other bolts, process them (e.g., filtering, aggregation, joining), and emit new tuples. Bolts can also store results in databases or pass them downstream for further processing.
• Topology: A topology defines the structure and flow of data in Storm. It is composed of spouts and bolts connected in a directed acyclic graph (DAG). Unlike batch jobs, a Storm topology runs continuously, processing data streams in real-time until explicitly stopped.
• Stream Groupings Stream groupings control how tuples are routed between spouts and bolts or between bolts. Common grouping strategies include shuffle grouping (random distribution), field grouping (based on specific field values), and global grouping (all tuples sent to a single bolt). Groupings help balance the load and maintain processing logic.

Apache Storm processes continuous, unbounded streams of data in real time. It handles each incoming record, called a tuple, as soon as it arrives without waiting to collect data in batches. This immediate, record-by-record processing minimizes delays and ensures ultra-low latency. It is useful in situations where instant decisions are essential, such as fraud detection, live monitoring, or real-time recommendation systems.

Processing Model

Apache Storm operates on a unique processing model that focuses on real-time data handling, allowing you to manage unbounded streams of data effectively. This model is essential for applications where instantaneous event-time processing is crucial.

• Tuple-Based Processing: Tuples are the basic data units at the core of Storm's processing model. Each tuple represents a single data record, which Storm processes as it flows through the system. This design enables you to handle each piece of data individually, providing real-time insights.
• Record-by-Record Handling: Unlike batch processing systems, Storm processes data on a record-by-record basis. This means that as soon as a tuple arrives, it is immediately processed by the bolts in the topology. This approach ensures that you can react to events without delay, making it ideal for scenarios like fraud detection or live analytics.
• Directed Acyclic Graph (DAG) Topologies: Storm uses DAGs to define the flow of data through its processing units. Understanding the types of graphs in data structure & applications can help you grasp why DAGs are ideal for such real-time data processing systems. Each topology consists of spouts (data sources) and bolts (processing units) connected in a way that allows data to flow continuously. This structure ensures that all incoming records are processed in real time until explicitly stopped by the user. 
• Stream Groupings: Storm efficiently distributes tuples among bolts by employing various stream groupings. These groupings determine how tuples are routed to different processing tasks. Common strategies include shuffle grouping (random distribution), field grouping (based on specific fields), and global grouping (all tuples sent to a single bolt). This flexibility allows you to optimize performance based on your application's needs.
• Micro-Batching Mechanism: While Apache Storm primarily supports true streaming capabilities, it can also utilize a micro-batching approach for certain operations. This allows for near-real-time processing by grouping small batches of tuples together. However, its strength lies in handling continuous streams effectively, making it suitable for applications requiring immediate responses.

Reliability & Fault Tolerance

Apache Storm is designed with exceptional reliability and fault tolerance mechanisms, making it suitable for mission-critical workflows. Its architecture ensures that data processing continues seamlessly, even during node failures. This unique capability makes Apache Storm a good choice for applications that require high availability and data integrity.

• Guaranteed Message Processing: Storm provides a reliable message processing framework that ensures every tuple (data record) is processed at least once. This is achieved through a system of “acker” tasks that track the lifecycle of tuples in the topology. When a tuple is emitted, it is assigned a unique ID, allowing Storm to monitor its status and confirm successful processing through acknowledgments. This mechanism prevents data loss, which is necessary for applications like financial transactions where accuracy is paramount. Developers use Storm Trident to handle complex event processing, aggregations, and transactions in distributed systems.
• Automatic Recovery: Storm’s architecture allows for automatic recovery in the event of worker failures. If a worker node dies, the supervisor daemon will restart it on another node without disrupting the system. This fail-fast design ensures that any unexpected issues do not impede ongoing data processing tasks. The stateless nature of the Nimbus and Supervisor daemons further enhances this resilience, as they can quickly restart without retaining prior state information.
• ACID Compliance: Apache Storm supports ACID (Atomicity, Consistency, Isolation, Durability) properties for its transactions, ensuring that all operations are completed reliably. This compliance plays a significant role in maintaining data integrity in critical applications like banking and healthcare, where minor inconsistencies can lead to major issues. For those unfamiliar, ACID properties in DBMS tutorials provide a clear understanding of how these principles safeguard data integrity. 
• Task Reassignment: When a worker fails to respond or continuously fails to start, Nimbus reassigns its tasks to other available workers in the cluster. This dynamic reassignment helps maintain operational continuity and ensures that no processing tasks are left unattended. Storm’s ability to handle such failures without losing data or halting operations distinguishes it from other real-time processing frameworks.

Programming Languages Used

Apache Storm is highly versatile, supporting multiple programming languages for developing its components, such as spouts and bolts. This flexibility allows developers to choose the languages that best fit their team’s expertise and project requirements. Let’s explore some of the most common programming languages supported by Apache Storm:

• Java: As the primary language for Apache Storm, Java provides excellent performance and extensive libraries for building real-time data processing applications. If you’re familiar with Java, you can easily leverage its capabilities to create efficient Storm topologies. The integration with Java also allows you to use existing Java frameworks and tools seamlessly. Those just starting out can benefit from a Java tutorial for beginners to gain the foundational skills needed to work with Storm effectively.
• Clojure: Storm is predominantly written in Clojure, a functional programming language that runs on the Java Virtual Machine (JVM). Its immutability and concurrency features make it well-suited for handling complex data processing tasks. If you prefer a functional programming approach, Clojure can offer unique advantages when developing Storm applications.
• Python: Through Storm’s multi-lang API, you can use Python to develop spouts and bolts. This support helps developers who prefer Python’s simplicity and readability contribute to real-time data processing projects. Moreover, Python’s extensive libraries for data analysis and machine learning can also enhance Storm applications. For quick reference while coding, many developers find a Python cheat sheet helpful to streamline their workflow. 
• Other Languages: Beyond Java, Clojure, and Python, Storm supports various other languages, such as Ruby and JavaScript, through the multilang protocol. These languages are supported through adapters that implement the JSON-based protocol over stdin/stdout, making Ruby or JavaScript integration into Storm topologies easier.

Want to master real-time data processing? Join upGrad’s Executive Diploma in Data Science from IIITB and gain expertise in handling streaming data with Apache Storm and Spark.

Apache Spark Overview in 2025

Apache Spark is designed for high-speed data processing and analytics. It provides a unified platform for various tasks, including batch processing, real-time analytics, machine learning, and graph processing. This makes it an indispensable tool for organizations seeking to extract valuable insights from massive datasets. Whether you're a data scientist, engineer, or analyst, Spark empowers you to tackle complex data challenges with ease and efficiency. Its in-memory processing capabilities make it significantly faster than traditional disk-based alternatives. Here’s how Apache Spark helps in data processing:

Unified Engine for Batch & Micro-Batch Processing

Apache Spark functions as a unified engine that adeptly handles both batch and micro-batch processing, making it an essential tool for modern data analytics. This dual capability contributes to managing large datasets and addressing real-time data processing needs. Let’s see how it works:

• Batch Processing: In traditional batch processing, data is collected over a period and processed as a group. Spark excels in this area by allowing you to perform complex transformations and aggregations on large datasets. This is particularly useful for ETL (Extract, Transform, Load) operations and historical data analysis, where you can process vast amounts of data at once.
• Micro-Batch Processing: Spark Structured Streaming introduces a micro-batch processing model, where incoming data streams are divided into small batches for processing. This approach enables near-real-time analytics by allowing you to process data in increments (as small as seconds). Micro-batching balances low latency and efficient batch processing, making it ideal for applications like fraud detection and IoT data handling.
• Unified API: One of Spark's key advantages is its unified API (Unified Application Programming Interface). This interface allows the use of the same codebase for batch and stream processing. This approach reduces complexity while enhancing productivity, enabling developers to focus on building applications without switching between different frameworks.
• Execution Model: In Spark, the micro-batch execution model processes each batch through several phases, including trigger execution and batch planning. Each micro-batch is processed sequentially while maintaining the ability to scale horizontally across distributed systems. This ensures that Spark can effectively handle high-volume data streams.

Also Read: Top 10 Apache Spark Use Cases Across Industries and Their Impact in 2025

Processing Model

Apache Spark employs a high-level processing model that leverages in-memory computing and a cluster-based architecture to optimize performance and efficiency. This model is fundamental to its ability to handle both batch and real-time data processing. Let’s understand how this processing model works:

• Micro-Batching with Spark Structured Streaming: For real-time data processing, Spark uses Structured Streaming, which treats incoming data as a series of small batches (micro-batches). This allows you to process streaming data using the same high-level APIs as batch processing, enabling seamless integration of real-time analytics into your applications.
• Resilient Distributed Datasets (RDDs): At the core of Spark's processing model are RDDs, which are fault-tolerant collections of data that can be distributed across the cluster. RDDs enable users to perform complex transformations and actions on data while maintaining high performance. They can be created from existing data sources or transformed from other RDDs, allowing for flexible data manipulation.
• Cluster Computing Model: Spark operates on a master-worker architecture, where tasks are distributed across multiple worker nodes. The driver program manages task execution, coordinating with the cluster manager to resource management efficiently. This distributed approach allows Spark to process large datasets in parallel, significantly enhancing speed and scalability.
• In-Memory Computing: One of Spark's key advantages is its ability to perform in-memory computations. By keeping intermediate data in memory rather than writing it to disk, Spark dramatically speeds up processing times, often achieving speeds 10 to 100 times faster than traditional disk-based systems like Hadoop³⁴. This capability is particularly beneficial for iterative algorithms and machine learning applications, where multiple passes over the same data are common.
• Lazy Evaluation: Spark uses lazy evaluation for its transformations, meaning it does not execute operations until an action is called. This approach helps optimize execution plans by minimizing unnecessary computations and reducing redundancy. When you perform a transformation on an RDD, Spark builds a Directed Acyclic Graph (DAG) of the operations but only executes them when an action (such as count or collect) is invoked.

Reliability & Fault Tolerance

Apache Spark is built with robust reliability and fault tolerance mechanisms, ensuring that data processing continues smoothly even when the system fails. This capability is essential for applications that require high availability and data integrity.

• Resilient Distributed Datasets (RDDs): Each RDD maintains lineage information that records the sequence of transformations applied to create it. If a partition of an RDD is lost due to a worker node failure, Spark can automatically recompute that partition from the original dataset using the lineage information. This allows for efficient recovery without losing any data.
• Checkpointing: Spark employs a checkpointing mechanism that periodically saves the state of RDDs to stable storage. This is particularly useful for long-running applications or streaming jobs where data loss would be detrimental. In case of a failure, Spark can restore its state from the last checkpoint, minimizing data loss and ensuring continuity.
• Write-Ahead Logs (WAL): For Spark Streaming applications, Write-Ahead Logs ensure that all incoming data is saved to durable storage before processing. This guarantees that no data is lost if the driver node fails. When a failure occurs, Spark can recover by reading from these logs and reapplying the operations intended for the lost data.
• Automatic Recovery: In the event of worker node failures, Spark automatically relaunches executors and restarts receivers as needed. This ensures that processing can continue without manual intervention. The system's design allows for parallel recovery by recomputing RDDs on input data from reliable sources, maintaining high availability.
• High Availability Architecture: Spark can be deployed in a highly available configuration using cluster managers like Apache Mesos or Hadoop YARN. These managers maintain backup nodes and facilitate quick recovery from failures, ensuring minimal downtime and consistent performance across distributed environments.

Programming Languages Used

Apache Spark supports a variety of programming languages, allowing developers to choose the one that best fits their skills and project requirements. This flexibility is one of the key features that makes Spark a popular choice for big data processing. Explore this Apache Spark Tutorial to understand how to optimize performance and handle massive datasets seamlessly.

Here are the key programming languages that Apache Spark supports:

• Scala: As the primary language for Apache Spark, Scala is highly favored among Spark developers. Spark itself is written in Scala, providing seamless access to all its features. Scala's strong typing and functional programming capabilities make it ideal for writing clean and efficient Spark applications. Many tutorials and examples are also available in Scala, making it easier for new developers to learn and implement Spark.
• Python (PySpark): Python is another popular choice, especially among data scientists and analysts. PySpark allows you to leverage Python's simplicity and extensive libraries while working with Spark. This makes it an excellent option for those familiar with Python, as transitioning from libraries like Pandas to PySpark can be quite natural. The ease of use and readability of Python code often make it the preferred language for quick prototyping and data analysis tasks.
• Java: Since Spark runs on the Java Virtual Machine (JVM), Java is also a supported language. While it performs well, developers may find that working with Java requires more boilerplate code compared to Scala or Python. Additionally, fewer resources and community examples are available in Java, which can make learning and troubleshooting more challenging. If you're new to programming, check out this What is Java? tutorial to learn the basics of the language.
• R (SparkR): SparkR supports R, allowing data scientists to use R's statistical capabilities alongside Spark's distributed processing power. While not as commonly used as Scala or Python, R can be beneficial for specific machine-learning tasks where existing R libraries are utilized.
• Other Languages: Apache Spark also supports third-party languages like .NET (C#) and Julia, allowing teams with expertise in these languages to integrate with Spark. However, these options may not be as mature or widely adopted as the core languages.

Here’s a comparison table for native Scala/Java support vs. Python (PySpark) and R (SparkR) integrations:

Looking to boost your big data skills? Enroll in upGrad’s Executive Post Graduate Program in Data Science & Machine Learning program and learn how to process real-time data efficiently!

Core Differences: Apache Storm vs Spark in 2025

When it comes to real-time data processing, Apache Storm and Apache Spark are strong contenders, but they operate with fundamentally different approaches. Choosing between them depends heavily on your specific needs and priorities. Storm excels at true real-time, low-latency processing of continuous data streams, while Spark offers powerful analytical capabilities with slightly higher latency. Understanding their core differences will enable you to make the right choice for your project. Here’s a big data tools comparison, including their processing models, fault tolerance, and language flexibility.

Processing Model & Latency

The most significant difference between Storm and Spark lies in their processing models. Storm is designed for true real-time processing, while Spark uses micro-batching. This distinction leads to different latency characteristics. Let's compare the Apache Storm vs Spark:

Fault Tolerance Mechanisms

Both Storm and Spark have robust fault tolerance mechanisms, but they differ in how they achieve data recovery and reliability. Storm focuses on per-record ACID compliance, while Spark relies on RDD lineage and checkpointing. Let's break the fault tolerance mechanism between Apache Storm vs Spark:

Language Flexibility & Developer Experience

When choosing a framework, language support and developer experience are key considerations. Storm is primarily Java-centric, while Spark offers a polyglot ecosystem, making it accessible to a broader range of developers. Let’s compare Apache Storm vs Spark language flexibility & developer experience:

Build a strong career in AI-driven data analytics! Join upGrad’s Professional Certificate Program in Data Science and Business Analytics Program and gain hands-on experience in real-time data management.

Choosing the Right Tool for 2025 Projects

Selecting the right tool for your big data projects in 2025 is crucial for success. Apache Storm vs Spark both have powerful frameworks, but they cater to different needs. Exploring their strengths and weaknesses will help you make informed decisions. The choice depends on your project's specific requirements, such as real-time processing needs, the complexity of data transformations, and the desired analytical capabilities. Let's delve into when to choose each tool and explore the potential of hybrid approaches:

When to Pick Apache Storm

Apache Storm excels when your project demands millisecond responses and real-time data processing. If you're dealing with applications that require immediate action, Storm might be your best bet. Here’s when to pick Apache Storm:

• Telecom Monitoring: In the telecom industry, you need to monitor network performance and detect anomalies instantly. Storm can process call data records in real-time, allowing you to identify and address issues before they impact users.
• Emergency Alerts: For emergency response systems, every second counts. Storm can process sensor data from various sources, such as weather stations and traffic sensors, to trigger immediate alerts in case of emergencies.
• Fraud Detection: Financial institutions must detect fraudulent transactions as they occur. Storm can analyze transaction streams in real time, flagging suspicious activities for immediate investigation.
• Real-Time Gaming: Online gaming platforms require immediate processing of player actions to provide a seamless experience. Storm can handle high-velocity data streams from game servers, ensuring low-latency responses and fair gameplay.

When to Pick Apache Spark

Apache Spark is ideal for unified pipelines that require machine learning, SQL, and both batch and streaming capabilities within a single framework. It's a versatile tool for a wide range of data processing tasks. Here’s when to pick Apache Spark:

• Machine Learning Pipelines: If your project involves complex machine learning models, Spark's MLlib library provides a comprehensive set of algorithms for building and training models. You can seamlessly integrate these models into your data processing pipelines.
• SQL Analytics: Spark SQL allows you to query and analyze structured data using SQL-like syntax. This makes it easy to perform ad hoc analysis and generate reports on large datasets.
• Batch Processing: Spark can efficiently process large batches of data, making it suitable for tasks such as data warehousing and ETL (Extract, Transform, Load) processes.
• Streaming Analytics: Apache Spark Streaming extends its capabilities to handle real-time data streams. While it doesn't offer the same low latency as Storm, it provides powerful tools for stream processing and analytics.

Read More: Apache Spark Streaming Tutorial

Hybrid Approaches 

Combining Apache Storm for real-time data ingestion and Apache Spark for batch analytics creates a powerful layered architecture. This hybrid approach allows organizations to use the strengths of both frameworks. Here’s how this hybrid approach works:

• Real-Time Ingestion with Storm: Apache Storm excels at low-latency data processing, making it ideal for real-time streaming data ingestion. It continuously collects and processes data from various sources, such as sensors or social media feeds, ensuring you react immediately.
• Batch Analytics with Spark: Once the data is ingested through Storm, Apache Spark can be used for batch analytics. Spark’s ability to process large datasets in memory allows for fast computations and complex transformations. It provides deeper insights into historical data trends and patterns.
• Seamless Integration: Storm and Spark integration can be achieved through various connectors and APIs. This allows you to pass data from Storm to Spark for further analysis easily and ensures a seamless flow of information between real-time and batch-processing systems.
• Scalability and Flexibility: One advantage of this hybrid architecture is scalability. Since both Storm and Spark can be deployed across distributed systems, you can easily scale up or down based on data volume. This ensures that your infrastructure can handle varying workloads without compromising performance.
• Comprehensive Insights: By leveraging both frameworks, organizations can gain a complete view of their data. Real-time processing enables immediate responses to events, while batch analytics provide a broader context through historical analysis, leading to informed decision-making.

Hybrid approaches are particularly beneficial in various industries. For example, in finance, organizations can use batch processing for end-of-day reporting while employing real-time analytics for fraud detection. Similarly, in e-commerce, businesses can analyze historical purchasing patterns while monitoring live customer behavior to personalize recommendations instantly.

Looking to enhance your skills in real-time processing with Apache Storm or build a comprehensive analytics pipeline with Apache Spark? upGrad provides the resources and expertise you need. upGrad offers a range of courses and certifications to help you master these essential big data tools. Let’s see what upGrad courses and certifications can help you upgrade your skills.

By enrolling in these programs, you can gain in-depth knowledge and hands-on experience, preparing you for a successful career in the rapidly evolving field of data science and big data. UpGrad's comprehensive curriculum and expert instructors ensure that you are well-equipped to tackle real-world challenges and drive innovation in your organization.

Future Trends Impacting Storm & Spark Adoption

As we move further into 2025, several emerging trends are influencing the adoption and application of Apache Storm vs Spark. Although these technologies work efficiently, key trends like the rise of edge computing and the increasing demand for real-time AI/ML are reshaping how businesses use them. That’s why understanding these trends is useful when choosing between them based on real-time and big data needs.

Let’s explore the key factors impacting Storm and Spark adoption:

Rise of Edge Computing

Edge computing, which brings computation and data storage closer to the source of data, is becoming more prevalent. This shift favors solutions that can operate efficiently in resource-constrained environments. Let’s understand how:

• Storm’s Lightweight Model: Apache Storm’s architecture is inherently lightweight, making it well-suited for cloud-native deployment on edge devices with limited resources. You can use Storm to process data closer to its origin, reducing latency and bandwidth usage.
• Spark’s Resource Constraints: Spark, with its higher resource requirements, often faces challenges when deployed on edge devices with limited processing power and memory. This can make Spark less practical for real-time processing at the edge.
• Implications for Adoption: As edge computing grows, Storm may see increased adoption in IoT and other applications where real-time processing at the edge is crucial. This trend may limit Spark’s direct use in similar edge-based scenarios.

Demand for Real-Time AI/ML

The increasing demand for real-time AI and ML is another trend impacting big data technologies. While Spark offers MLlib, a machine learning integration library, Storm relies on external integrations, such as TensorFlow, for its ML capabilities. Let’s understand how real-time AI/ML demand reshapes Apache Storm vs Spark adoption:

• Spark’s MLlib: Spark offers MLlib, a built-in machine learning library that provides a comprehensive suite of algorithms and tools for developing ML models. This integration simplifies the process of applying machine learning to batch data and some streaming applications.
• Storm’s Need for External ML Integration: To incorporate ML capabilities, Storm typically requires integration with external machine learning frameworks, such as TensorFlow or H2O.ai. This necessity demands careful architectural planning.

Additionally, Storm’s approach offers the flexibility to use cutting-edge ML technologies, while Spark’s integrated MLlib provides convenience and ease of use. Therefore, you can choose a framework depending on your big data processing requirements.

Serverless and Managed Services

Serverless computing and managed cloud services are streamlining big data operations by reducing costs while enhancing scalability. These solutions allow you to focus on building applications rather than managing infrastructure. Here are some examples of serverless and managed services that reduce operational complexity:

• AWS Glue (Spark): AWS Glue, a fully managed ETL (extract, transform, load) service, simplifies the process of running Spark jobs in the cloud. You can leverage Glue to reduce the operational burden associated with managing Spark clusters and infrastructure.
• Confluent Cloud (Storm): Confluent Cloud offers a managed Apache Kafka service, which often complements Storm for building real-time data pipelines. You can offload the management of your Kafka infrastructure to Confluent, streamlining your operations.
• Operational Efficiency: As serverless and managed services evolve, you will likely see increased adoption of these platforms to reduce operational complexity. Whether you choose AWS Glue for Spark or Confluent Cloud with Storm, these services enable you to focus on your applications and insights rather than infrastructure management.

Upgrade your skills in machine learning and real-time data processing! Enroll in upGrad’s Executive Diploma in ML & AI from IIIT B (Executive) and learn to implement streaming frameworks like Apache Spark.

How upGrad Can Help?

Want to learn from the best? upGrad offers industry-relevant certification programs designed to help professionals excel in big data technologies like Apache Storm and Apache Spark. With expert mentorship, hands-on training, and career support, learners can bridge skill gaps. Our practical-oriented programs enhance candidates' employability and help them secure high-paying roles in data engineering, analytics, and cloud computing. Let’s explore how upGrad can help you upskill in data technologies:

Industry-Aligned Certification Programs

upGrad provides specialized courses to help professionals become experts in real-time and batch processing. Our certificate programs are designed in collaboration with industry leaders and academic experts to ensure practical relevance and market alignment. Here are our top programs to help you tackle real-world challenges:

Mentorship and Networking Opportunities

upGrad connects you with industry experts and experienced mentors who guide you throughout your learning journey. You’ll gain insights into best practices, emerging trends, and career strategies. Additionally, networking with alumni and professionals increases job opportunities and helps with salary negotiations. With upGrad, you gain access to hiring managers and recruiters looking for skilled big data professionals.

Career Transition Support

We provide comprehensive career support to help you transition into big data roles effectively. Here’s how upGrad’s career support services assist learners in securing top positions in tech companies:

• Resume Building: Our experts help you showcase your Storm and Spark projects effectively, guiding you in highlighting relevant technical skills and project experience. If you’re unsure where to start, resources like how to build a resume for your dream job can provide valuable tips to structure and polish your resume.
• Interview Preparation: Structured preparation sessions cover common technical questions about Storm and Spark. Practice sessions include system design discussions and coding challenges.
• Industry Partnerships: Direct connections with technology companies seeking Storm and Spark expertise. Regular hiring drives and placement assistance with leading organizations.
• Job Role Alignment: Guidance on choosing between stream processing engineer and data engineer roles. Assistance in identifying the most suitable career path based on your skills.

Bottom Line

As the demand for robust data processing solutions grows, both Apache Storm and Apache Spark offer unique advantages. There is no doubt that both frameworks are dominant in the big data arena. However, choosing between Apache Storm vs Spark requires careful consideration. Spark’s machine learning capabilities and ease of use are compelling, while Storm offers unmatched real-time stream processing. Professionals equipped with knowledge of both frameworks are well-positioned to address diverse data challenges effectively. Consider your project’s real-time needs and processing style before selecting one, or opt for a hybrid approach to harness their full potential.

Ready to master these technologies? Explore upGrad’s Big Data course to enhance your skills.

Boost your career with our popular Software Engineering courses, offering hands-on training and expert guidance to turn you into a skilled software developer.

Explore our Popular Software Engineering Courses

Master in-demand Software Development skills like coding, system design, DevOps, and agile methodologies to excel in today’s competitive tech industry.

In-Demand Software Development Skills

Stay informed with our widely-read Software Development articles, covering everything from coding techniques to the latest advancements in software engineering.

Read our Popular Articles related to Software

References:

https://www.statista.com/statistics/871513/worldwide-data-created/#:~:text=Amount%20of%20data%20created%2C%20consumed,2023%2C%20with%20forecasts%20to%202028&text=The%20total%20amount%20of%20data,home%20entertainment%20options%20more%20often.
https://www.statista.com/statistics/1258671/big-data-processing-software-market-share-technology-worldwide/
https://www.upgrad.com/blog/big-data-tools/
https://www.upgrad.com/blog/apache-storm-overview/
https://www.upgrad.com/blog/apache-spark-features/
https://www.upgrad.com/blog/apache-spark-tutorial-beginners/
https://www.whizlabs.com/blog/apache-storm-vs-apache-spark/
https://mindmajix.com/apache-spark-vs-apache-storm
https://data-flair.training/blogs/apache-storm-vs-spark-streaming/
https://phoenixnap.com/kb/apache-storm-vs-spark
https://www.tutorialspoint.com/apache-storm-vs-spark-side-by-side-comparison
https://www.simplilearn.com/tutorials/big-data-tutorial/apache-strom
https://www.edureka.co/blog/spark-architecture/