Comparing Apache Spark Alternatives: Apache Storm, Apache Flink, Apache Hadoop, Apache Beam, Snowflake, and More.

Apache Spark is a juggernaut in the big data world—a truly versatile, open source engine known for handling massive data processing jobs quickly. It's popular for batch processing, streaming, machine learning, and more. But let's face it, no tool is a silver bullet. Spark is powerful, sure, but it has its quirks and limits. Why? Maybe you're wrestling with memory issues, find performance tuning tricky, or need even faster, real-time responses than Spark's streaming offers. These are legitimate reasons to see what else is cooking. The only way to address these issues is by exploring Apache Spark alternatives. But remember, ditching Apache Spark entirely isn't worth it because it still is one of the most powerful big data processing frameworks out there.

In this article, we'll quickly touch on what Apache Spark does and where it sometimes stumbles. Then, we'll explore 7 significant Apache Spark alternatives, looking at what they do best, where they differ from Apache Spark, and when you might choose them instead.

Apache Spark 101—The Basics and Bumps Leading to Apache Spark Alternatives

Before jumping into Apache Spark alternatives, let's get on the same page about Spark.

What Is Apache Spark and What Is It Used For?

Apache Spark is a powerful,high-performance, open source distributed computing engine designed for large-scale data analytics. It supports a wide range of workloads, including batch processing, real-time analytics, machine learning, and graph processing. Apache Spark's in-memory computing capabilities make it up to 100x faster than traditional Apache Hadoop MapReduce operations, especially when handling large datasets.

Apache Spark's core engine manages distributed task execution, memory utilization, and fault recovery across a cluster. It operates using a master-worker architecture, typically involving a Driver Program (which hosts the SparkContext and orchestrates the job), a Cluster Manager (like YARN, Kubernetes, Mesos, or Spark's standalone manager), and Executor processes (running on worker nodes, executing tasks and storing data).

Originally, Spark's primary data abstraction was the Resilient Distributed Dataset (RDD). RDDs are immutable, partitioned collections of records that can be processed in parallel across cluster nodes. They achieve fault tolerance through lineage: each RDD remembers the sequence of transformations (represented as a Directed Acyclic Graph (DAG)) used to create it from a fault-tolerant data source. If a partition is lost (e.g: due to a node failure), Spark can use the lineage to recompute just that partition.

While RDDs remain the foundation, Spark now primarily promotes higher-level, optimized APIs: DataFrames and Datasets.

These structured APIs (DataFrames and Datasets) are generally preferred over RDDs for most common use cases involving structured or semi-structured data due to their ease of use and performance optimizations. RDDs are still valuable for unstructured data or when fine-grained control over physical execution is needed.

One of Spark's key strengths is its support for multiple programming languages through APIs for Java, Scala, Python, and R. Also, Spark integrates seamlessly with a vast array of data sources, including distributed file systems (like HDFS, Amazon S3, Azure Blob Storage), relational databases (via JDBC/ODBC), NoSQL databases (such as Cassandra, HBase…), message queues (like Kafka, Kinesis), and various file formats (such as Parquet, ORC, JSON, CSV).

Apache Spark comes with a rich set of built-in libraries that cater to various data processing needs:

• Spark SQL: Enables fast, distributed SQL queries for data analysis.
• Spark MLlib: Provides machine learning algorithms and utilities.
• Spark GraphX: Supports graph processing and analytics.
• Spark Streaming: Allows real-time data processing and analytics.

Check out this video if you want to learn more about Apache Spark in depth.

TL;DR: Apache Spark is a versatile and powerful unified engine for large-scale data analytics, offering significant speed advantages (especially with in-memory processing), fault tolerance, and a rich set of APIs and libraries for batch, streaming, ML, and graph workloads.

Here are some of the key main features of Apache Spark:

➥ In-Memory Computation Preference — Spark attempts to load data into memory and cache intermediate results, drastically reducing disk I/O bottlenecks and accelerating iterative and interactive workloads compared to disk-based systems like MapReduce.

➥ Fault Tolerance — Achieved primarily through RDD lineage (tracked via the DAG), allowing Spark to automatically recompute lost data partitions on worker node failures. Checkpointing (saving intermediate data to persistent storage) can be used to truncate lineage for very long-running jobs or iterative algorithms.

➥ Lazy Evaluation — Transformations on RDDs/DataFrames/Datasets are lazily evaluated. Spark builds up a DAG of operations and only executes them when an action (e.g., count(), collect(), writing to storage) is called. This allows the Catalyst optimizer (for DataFrames/Datasets) to optimize the overall execution plan.

➥ Low-Latency Stream Processing — Structured Streaming provides a high-level API for continuous data processing using a micro-batch or (experimentally) continuous processing model, offering fault tolerance and exactly-once processing semantics (in most cases with appropriate sinks).

➥ Unified Engine — Supports diverse workloads—batch processing, interactive SQL queries (via Spark SQL), stream processing (via Structured Streaming), machine learning (via MLlib), and graph processing (via GraphX/GraphFrames)—within a single framework and often using unified APIs (especially DataFrame/Dataset).

➥ Multi-Language APIs — Provides APIs for Scala, Java, Python, R, and a powerful SQL interface, catering to various developer preferences and skillsets.

➥ Advanced Analytics Libraries — Includes built-in libraries like MLlib for machine learning and GraphX for graph analytics, simplifying the development of complex analytical applications.

➥ Extensible Data Source API — Connects to a wide variety of data storage systems (filesystems, databases, key-value stores, message queues) through built-in and third-party connectors.

Check out this article if you want to learn more in-depth about how Apache Spark works and its architecture overview.

Where Apache Spark Can Stumble—Why Look for Apache Spark Alternatives?

Okay, Apache Spark is impressive. But where might it cause friction? 

1) High Memory Consumption

Apache Spark processes data quickly largely because it tries to keep data in RAM across your cluster nodes. Handling large datasets means you'll need nodes with significant amounts of memory, which can increase costs.

2) Out-of-Memory (OOM) Errors

Apache Spark can run out of memory on the driver or executor nodes if you don't allocate enough, if data partitions are skewed, or during large data shuffles. Debugging these OOM errors when they happen can take time.

3) Garbage Collection (GC) Pauses

Apache Spark runs on the Java Virtual Machine (JVM). When working with large amounts of memory, the JVM's garbage collection process can cause pauses, slowing down or temporarily stopping your job's execution. Tuning GC is possible but often complex.

4) Spilling to Disk

Apache Spark writes data to disk if it doesn't fit into the available RAM. While this prevents out-of-memory errors, reading and writing from disk is much slower than accessing RAM, significantly impacting your job's performance.

5) Learning Curve

Apache Spark has user-friendly high-level APIs like SQL and DataFrames. However, writing truly efficient code and troubleshooting performance often requires you to understand its core distributed processing concepts like partitioning, shuffling, and lazy evaluation, which takes effort to learn.

6) Configuration Complexity

Apache Spark offers many configuration parameters (hundreds) controlling memory, parallelism, serialization, and more. Finding the optimal settings for your specific workload and cluster usually requires expertise and experimentation.

7) Debugging Difficulty

Apache Spark runs jobs across multiple machines. Pinpointing the cause of failures or slow tasks requires you to analyze logs and metrics from different nodes, making debugging more complex than for single-machine applications.

8) Dependency Management

Apache Spark requires you to make sure that any libraries your code uses (especially Python libraries) are available and have consistent versions on the driver and all worker nodes. Managing these dependencies across a cluster can be tricky.

9) Expensive Shuffle Operations

Apache Spark needs to redistribute data across nodes for operations like joining datasets or grouping by keys (a "shuffle"). This process involves writing data to disk, sending it over the network, and reading it back, making it a common performance bottleneck.

10) Inefficiency with Small Files

Apache Spark performs best when reading fewer, larger files. If your dataset consists of many small files, Apache Spark may create too many small tasks, leading to scheduling overhead and potentially straining the driver node's resources.

11) Micro-Batch Streaming Latency

Apache Spark Structured Streaming processes streaming data in frequent small batches, not one event at a time. This micro-batch approach introduces inherent latency (often seconds or high milliseconds), making it unsuitable if you need true real-time processing with millisecond response times.

12) Interactive Query Latency

Apache Spark has some startup overhead for launching jobs. While fast for large-scale analytics, it might not always meet the sub-second response times needed for highly interactive data exploration compared to specialized analytical databases.

13) Cluster Management Overhead

Apache Spark requires a cluster manager (like YARN, Kubernetes, or its own standalone manager). Setting up, maintaining, monitoring, securing, and upgrading this cluster infrastructure requires ongoing operational effort.

14) Resource Costs

Apache Spark often needs substantial CPU and particularly RAM resources to run efficiently on large datasets. This directly impacts the cost of your cloud instances or on-premises hardware.

15) Reliance on External Storage

Apache Spark is a processing engine, not a storage system. You need to use it with a separate distributed storage system like HDFS, Amazon S3, Azure Data Lake Storage, or Google Cloud Storage, adding another component to your architecture.

16) Unsuitability for Transactional Workloads (OLTP)

Apache Spark is designed for complex analytical queries over large volumes of data (OLAP). It's not built for the high rate of small, indexed reads and writes typical of online transaction processing (OLTP) systems, which databases handle better.

17) Potential Overkill for Small Data

Apache Spark's distributed nature introduces overhead. If your data can be processed effectively on a single machine using libraries like Pandas or Polars, setting up and running an Apache Spark cluster might be unnecessary complexity.

So, if you're bumping into these issues, or if your needs are highly specialized, looking at Apache Spark alternatives makes perfect sense.

So, if you're bumping into these issues, or if your needs are highly specialized, looking at Apache Spark alternatives makes perfect sense.

7 Popular Apache Spark Alternatives—Which Will You Pick?

Now, let's check out some other players in the big data processing game. We'll look at what they are, how they differ from Apache Spark, and where they shine.

1) Apache Spark Alternative 1—Apache Storm

Apache Storm is a free, open source, distributed real-time computation system. It is designed to reliably process unbounded streams of data—data that arrives continuously with no defined end—at high velocity. Think of it as a powerful tool specifically built for handling data "in motion," contrasting with systems like Hadoop MapReduce which are primarily designed for batch processing of data "at rest".

Apache Storm allows you to process potentially very large volumes of incoming data, sometimes over a million data units (called tuples) per second on each machine in your cluster. Apache Storm achieves this through a distributed architecture. It can scale horizontally by adding more machines to the cluster to increase processing capacity. Storm is also inherently fault-tolerant; if a worker node or process fails, the system automatically reassigns its tasks to other available nodes. ou define the data processing logic using a "topology," which is a directed acyclic graph (DAG) connecting data sources called "Spouts" and data processors called "Bolts". Storm guarantees that each incoming tuple will be processed at least once. It finds applications in real-time analytics, online machine learning, continuous computation, distributed RPC, and ETL processes.

Apache Storm Architecture

Now, let's take a closer look at the Apache Storm architecture. To build reliable stream processing apps, you need to know what's under the hood. Apache Storm is a distributed system, and it's made up of a few key components that work together seamlessly.

When you build a Apache Storm application, you interact with these fundamental abstractions:

➥ Streams — A stream is the fundamental data structure in Apache Storm. It's an unbounded sequence of tuples flowing continuously.

➥ Tuples — A tuple is an ordered list of values, representing a single record or event being processed. Each value can have a defined type.

➥ Spouts — A Spout is the source of streams in your topology. It reads data from an external source (like Kafka, a database, or an API) and emits it as tuples into the topology.

➥ Bolts — A Bolt processes incoming streams of tuples. It receive streams of tuples, perform computations (such as filtering, aggregation, joining data, interacting with databases, or running machine learning models), and can optionally emit new tuples to downstream bolts.

➥ Topology — A Topology is a network (specifically, a Directed Acyclic Graph or DAG) connecting Spouts and Bolts, defining the complete data flow and processing logic. Once deployed, a topology runs continuously until explicitly stopped.

Runtime Components: How Apache Storm Runs Your Topology

These components manage the execution of your topology across the cluster:

➥ Nimbus — The master node controller for an Apache Storm cluster, analogous to Hadoop's JobTracker. You submit your topology code (typically packaged as a JAR file) to Nimbus. Nimbus analyzes the topology, distributes the code to worker nodes, assigns tasks, monitors their execution, and reallocates tasks upon failure. Nimbus itself does not process data tuples; it orchestrates the cluster. Nimbus is designed to be stateless and fail-fast, storing its state in ZooKeeper.

➥ Zookeeper — Apache Storm relies heavily on Apache ZooKeeper for coordination across the cluster. ZooKeeper manages the cluster state, including task assignments, node heartbeats, and configuration. Nimbus writes state information to ZooKeeper, and Supervisors read it to understand their assigned tasks. This reliance on ZooKeeper makes the cluster resilient to Nimbus failures, as a restarted Nimbus can recover state from ZooKeeper.

➥ Supervisor — A daemon running on each worker node in the cluster. The Supervisor listens for work assigned to its machine via ZooKeeper. Following Nimbus's instructions (retrieved from ZooKeeper), it starts and stops worker processes on its local node as required to execute parts of the topology. Supervisors are also designed to be stateless and fail-fast.

➥ Worker Process — A Java Virtual Machine (JVM) process running on a worker node that executes a subset of a topology. Each worker process runs one or more executors for different components (Spouts or Bolts).

➥ Executor — An executor is a single thread spawned by a worker process. It runs one or more tasks for a specific spout or bolt.

➥ Task — A task performs the actual data processing—it's an instance of your Spout or Bolt logic. A single Spout or Bolt in your topology definition can be executed as many parallel tasks across the cluster, spread across different executors and workers.

How It All Fits Together

You define a Topology (Spouts + Bolts + Streams) ➤ Submit it to Nimbus ➤ Nimbus plans execution and uses ZooKeeper for coordination ➤ Supervisors see assignments in ZooKeeper and start Workers ➤ Workers start Executors ➤ Executors run Tasks ➤ Spouts emit data, Bolts process it ➤ ZooKeeper helps manage state and failures.

Apache Storm Features

Here are the key features of Apache Storm:

1) Real-time Processing — Apache Storm handles data continuously as it arrives, allowing for low-latency computations. It's built for speed, capable of processing very high volumes of messages.

2) Scalability — Apache Storm is designed to run on clusters of machines. You can scale horizontally by adding more nodes to the cluster to increase processing capacity as data loads grow, without interrupting ongoing processes.

3) Fault Tolerance — Apache Storm automatically handles failures. If a processing node goes down, the system automatically redistributes the tasks assigned to that node to other available nodes, aiming for continuous operation.

4) Guaranteed Message Processing — Apache Storm guarantees that each incoming data record (tuple) is processed at least once. With additional configurations, like the Trident abstraction layer (though now often replaced by other approaches), exactly-once processing semantics can be achieved.

5) Language Flexibility — Primarily developed in Clojure and Java, but topologies can be defined in other languages using Storm's multi-lang protocol (via ShellSpout/ShellBolt adapters).

6) Simple Programming Model — Apache Storm uses basic components like "Spouts" (data sources) and "Bolts" (processing units) which connect together to form a "topology" (a directed graph defining the data flow and processing steps).

7) Integration — Apache Storm integrates with many common queueing systems (like Kafka) and database technologies, allowing it to fit into existing data infrastructure stacks.

8) Distributed Architecture — Apache Storm uses a master (Nimbus) / worker (Supervisor) architecture coordinated via ZooKeeper.

Pros of Apache Storm:

• Known for low latency, suitable for near real-time processing use cases
• Capable of handling very large data volumes
• Scales horizontally by adding nodes
• Automatic recovery from node/process failures maintaining computation continuity
• Provides at-least-once message processing guarantees; exactly-once semantics available via the Trident API.
• Supports topology development in multiple programming languages.
• Connects easily with popular queuing and database systems

Cons of Apache Storm:

• Can be complex to set up, configure, tune, monitor, and debug, especially in large clusters
• Like most distributed systems, requires significant CPU and memory resources
• Does not have sophisticated built-in resource management capabilities
• Core Storm API lacks some advanced features found in newer frameworks like Apache Flink or Spark Streaming (such as event-time processing, built-in advanced windowing capabilities, and unified batch/stream APIs)
• Less active development and community focus compared to Apache Flink or Apache Spark.
• Built-in state management is limited; usually needs external systems.

2) Apache Spark Alternative 2—Apache Flink

Apache Flink is another open source framework and a distributed processing engine. Its main job? Running stateful computations over data streams. Think of it like this: Flink processes data as it comes in (unbounded streams) but can also handle fixed datasets (bounded streams). It's built to run computations fast, using in-memory speed, and it can scale pretty massively across clusters. People use it for real-time analytics, building applications that react to events as they happen, and setting up continuous data pipelines.

Check out this article to learn more in-depth about Apache Flink architecture.

Apache Flink Features

Here are some key features of Apache Flink:

1) Unified Stream and Batch Processing — Apache Flink doesn't force you to choose separate tools for real-time data (streams) and historical data (batches). It uses the same engine for both. It sees batch processing basically as a special, finite case of stream processing.

2) Stateful Computations — Apache Flink is designed from the ground up for stateful computations. It can maintain state reliably (even across failures) which is absolutely necessary for many complex processing tasks like aggregations over time, detecting patterns (Complex Event Processing - CEP), or even certain machine learning applications. It keeps this state locally for speed and uses checkpoints for fault tolerance.

3) Event Time Processing — Apache Flink supports event time semantics, meaning it can process data based on the timestamps embedded within the data itself (when the event occurred). This allows for accurate results even if data arrives late or out of order, which is often the case in real-world scenarios. It also supports processing time (when Flink sees the data) and ingestion time (when data enters Flink).

4) Low Latency and High Throughput — Apache Flink is built for speed. It processes data point-by-point (or in micro-batches) with very low latency, often in the sub-second range. Its pipelined, distributed architecture allows it to achieve high throughput, processing large volumes of data concurrently across many nodes in a cluster. It uses in-memory computation where possible to keep things quick.

5) Fault Tolerance and Exactly-Once Semantics — Distributed systems can fail. Apache Flink is designed to handle this. It uses a mechanism called checkpointing – basically taking consistent snapshots of the application's state and stream position periodically. If something goes wrong, Flink can restart from the last successful checkpoint, making sure no data loss and guaranteeing that state updates are processed exactly once.

6) Flexible Deployment — You can run Flink pretty much anywhere. It integrates with common resource managers like Kubernetes and YARN, but you can also run it as a standalone cluster on your own machines (bare metal) or in the cloud.

7) Layered APIs — Apache Flink offers different levels of abstraction, letting you choose the right tool for the job.

• SQL / Table API — The highest level, offering declarative, relational APIs similar to standard SQL for both stream and batch data.
• DataStream API — The core API for stateful stream processing (available for Java, Scala, Python). It gives you more control over the stream processing logic, including windowing and state.
• ProcessFunction — The lowest-level abstraction (part of the DataStream API). It provides fine-grained control over time and state, allowing you to implement complex, custom processing logic.

8) Highly Scalable — Apache Flink applications can be parallelized across thousands of tasks distributed over many machines in a cluster. It's designed to scale horizontally, handling very large states (terabytes) and high data volumes efficiently.

Check out this article to learn more in-depth about the pros and cons of Apache Flink.

3) Apache Spark Alternative 3—Apache Hadoop (MapReduce)

You can't talk about big data history without mentioning Apache Hadoop. It's the open source framework that kicked off much of the large-scale data processing revolution. Its goal? Store and process massive datasets (gigabytes to petabytes) across clusters of standard, affordable hardware.

Apache Hadoop isn't really a direct processing competitor to Apache Spark in the same way Apache Flink is. The core of Apache Hadoop consists of four main modules working together. The Hadoop Distributed File System (HDFS) acts as the distributed storage layer, placing data directly onto the local storage of individual machines for high-speed access. Yet Another Resource Negotiator (YARN) manages the cluster's resources, scheduling tasks and allocating computation power. MapReduce provides the programming model for processing the data in parallel across the cluster nodes and combining the results. Hadoop Common contains the necessary libraries and utilities shared by the other modules, all designed assuming hardware failures are normal and should be handled automatically by the software.

Apache Hadoop Architecture

Now, let's take a closer look at the Apache Hadoop architecture.

Core Components of Apache Hadoop Architecture

Apache Hadoop architecture consists of four main components:

• Hadoop Distributed File System (HDFS) – handles distributed storage.
• Yet Another Resource Negotiator (YARN) – manages resources.
• MapReduce – processes data (though other frameworks can also be used).
• Hadoop Common – provides utilities and libraries supporting the other components.

Let’s break them down.

➥ Hadoop Distributed File System (HDFS)

This is the storage layer. HDFS is designed to store extremely large files reliably across many machines in a cluster. It breaks files into large blocks (typically 128MB or 256MB) and distributes replicas of these blocks across different machines.

• NameNode
  Think of this as the manager or index for the filesystem. It holds the metadata – information about the directory structure, file permissions, and the location of each block that makes up a file. It knows which DataNodes hold which blocks, but it doesn't store the actual data itself. There's typically one active NameNode per cluster (though High Availability configurations exist). 
• DataNodes
  These are the worker machines that store the actual data blocks. They regularly communicate with the NameNode, sending heartbeats and reports about the blocks they store. DataNodes perform the read and write operations requested by clients or processing tasks, fetching blocks as directed by the NameNode. Replication (usually 3 copies by default) across DataNodes provides fault tolerance; if one DataNode fails, the data is available on others.

➥ Yet Another Resource Negotiator (YARN)

Yet Another Resource Negotiator (aka YARN) is the resource management and job scheduling layer, introduced in Apache Hadoop 2. YARN separates the job scheduling/resource management function from the processing logic (which was handled by the JobTracker in Apache Hadoop 1). This allows Apache Hadoop to run different types of processing frameworks beyond MapReduce. 

• ResourceManager (RM)
  ResourceManager is the central authority that manages the cluster's resources (CPU, memory). It has two main parts: the Scheduler (which allocates resources to various running applications based on configured policies, without monitoring the application's tasks) and the ApplicationsManager (which accepts job submissions and manages the ApplicationMasters).
• NodeManager (NM)
  NodeManager runs on each worker machine (like DataNodes). It manages the resources available on that specific machine, monitors resource usage (CPU, memory, disk, network), and reports this information to the ResourceManager. It's responsible for launching and managing containers (allocated bundles of resources) on its machine as directed by the ResourceManager.
• ApplicationMaster (AM)
  When you submit a job (an application) to YARN, a specific ApplicationMaster process is started for that job. The AM negotiates with the ResourceManager's Scheduler for necessary resources (containers) and works with the NodeManagers to launch and monitor the tasks that make up the job within those containers.

➥ MapReduce

MapReduce is the original processing framework for Apache Hadoop. It provides a model for processing large datasets in parallel across the cluster. While YARN allows other frameworks (like Apache Spark), MapReduce remains a fundamental part of the Hadoop ecosystem.

MapReduce works in two main phases:

• Map Phase
  Input data (stored in HDFS) is divided into chunks. Multiple "Map" tasks run in parallel, each processing one chunk. A map task takes key-value pairs as input, applies your processing logic, and produces intermediate key-value pairs as output.
• Reduce Phase
  The intermediate results from the map phase are shuffled and sorted based on their keys. "Reduce" tasks then process the data associated with each key. A reduce task takes a key and a list of associated values, applies your aggregation or processing logic, and produces the final output, which is typically written back to HDFS. 

➥ Hadoop Common (or Common Utilities)

Hadoop Common provides essential Java libraries and utilities needed by the other Hadoop modules. It includes foundational elements for filesystems, remote procedure calls (RPC), and general utility classes that support the overall framework.

How It All Fits Together

When you want to process data with Apache Hadoop:

1) Your data is first stored in HDFS, broken into blocks and replicated across DataNodes.The NameNode keeps track of where everything is.

2) You submit your processing job (e.g: a MapReduce job) to YARN's ResourceManager.

3) The ResourceManager finds a NodeManager with available resources and launches an ApplicationMaster for your job.

4) The ApplicationMaster figures out what tasks need to run (Map tasks, Reduce tasks) based on the data's location in HDFS. It requests resource containers from the ResourceManager.

5) The ResourceManager grants containers on various NodeManagers (ideally, close to where the data blocks are stored).

6) The ApplicationMaster instructs the NodeManagers to launch the Map and Reduce tasks within these containers.

7) Tasks read data from HDFS, process it, and write output back to HDFS. The NodeManagers monitor the tasks, and the ApplicationMaster oversees the whole job, reporting progress back to you.

TL;DR: Your Hadoop cluster stores data in HDFS and processes jobs with MapReduce. YARN schedules resources for MapReduce tasks and any other processing frameworks you deploy. Hadoop Common offers consistent APIs, helping you integrate additional tools with the core architecture.

Apache Hadoop Features

Here are some key features of Apache Hadoop:

1) Distributed Processing — Apache Hadoop enables the parallel processing of large datasets across a cluster of computers. It breaks down big data and analytics jobs into smaller workloads that run simultaneously on different nodes.

2) Distributed Storage — Apache Hadoop uses the Hadoop Distributed File System (HDFS). HDFS is a distributed file system that provides high-throughput access to application data. It stores large files (gigabytes to terabytes) across multiple machines. HDFS achieves reliability through data replication. It provides shell commands and Java APIs similar to other file systems.

3) Fault Tolerance — Instead of relying on hardware for high availability, Apache Hadoop is designed to detect and handle failures at the application layer. Data is often replicated across multiple hosts to achieve reliability

4) Highly Scalable — The architecture of Apache Hadoop allows it to scale up from single servers to thousands of machines. Each machine contributes its local computation and storage resources. You can easily add more nodes to your cluster to handle growing data volumes.

5) Data Locality — Apache Hadoop tries to move the computation to the data, rather than moving large amounts of data across the network to the computation.

6) MapReduce — Apache Hadoop includes MapReduce, a parallel processing of large data sets. It is also a programming model that divides an application into small fractions to run on different nodes. Map tasks process input data and convert it into key-value pairs. Reduce tasks then aggregate the output to provide the desired result.

7) YARN (Yet Another Resource Negotiator) — YARN is a framework for job scheduling and cluster resource management. It manages computing resources in clusters and schedules user applications. YARN allows you to run various workloads on the same cluster, including interactive SQL, advanced modeling, and real-time streaming.

8) High Availability — Apache Hadoop keeps running with backup NameNodes (for HDFS) and ResourceManagers (for YARN) that step in during failures.

9) Security — Apache Hadoop offers authentication, authorization, and encryption to control access and protect data.

10) Ecosystem — Apache Hadoop ties into tools like Hive (SQL-like queries), Pig (data flow scripting), HBase (NoSQL database), and Apache Spark (in-memory processing) for broader functionality.

12) Data Format Flexibility — Apache Hadoop handles text, binary, and structured formats like Avro and Parquet for storage and processing.

13) Batch Processing — Apache Hadoop is built for large-scale batch jobs, like ETL (Extract, Transform, Load), with high efficiency.

14) Integration — Apache Hadoop Links with relational databases, NoSQL systems, and cloud storage for flexible data workflows.

Pros of Apache Hadoop:

• Scales horizontally. You can add more commodity hardware nodes to the cluster to handle growing data volumes without much downtime
• Being open-source, Hadoop eliminates software licensing costs
• Handles petabyte-scale datasets via distributed processing
• Data is automatically replicated across multiple nodes in the cluster (typically 3 copies). If a node fails, the work and data can be picked up by another node
• HDFS can store vast amounts of data in any format – structured, semi-structured, or unstructured
• Optimized for parallel batch processing of large datasets.

Cons of Apache Hadoop:

• HDFS is optimized for large files. Storing a large number of small files (smaller than the HDFS block size, often 128MB) is inefficient because each file's metadata consumes NameNode memory
• Hadoop's original processing engine, MapReduce, performs heavy disk I/O, writing intermediate results between the map and reduce phases to disk
• Core design is for high-latency batch jobs, not real-time or stream processing.
• HDFS is designed for high-throughput sequential reads, not fast random reads/writes required by some applications
• Setting up, configuring, tuning, managing, securing, and maintaining a Hadoop cluster requires significant expertise in distributed systems
• Being Java-based, it inherits potential Java vulnerabilities, and securing the distributed environment requires careful configuration and often third-party tools

4) Apache Spark Alternative 4—Apache Beam (Batch + strEAM)

What if you want to write your data processing logic once but run it on different engines like Apache Spark, Apache Flink, or Google Cloud Dataflow? That's the idea behind Apache Beam.

Apache Beam (Batch + strEAM) is an open source, unified programming model for defining and executing data processing pipelines. It supports both batch and stream (continuous) processing, making it versatile for various data workflows. Apache Beam provides a set of language-specific SDKs for constructing pipelines and runners for executing them on distributed processing backends. These backends include Apache Flink, Apache Spark, Google Cloud Dataflow, and Hazelcast Jet.

Apache Beam Architecture

Okay, now, let's look at the Apache Beam architecture. The main idea behind Apache Beam is to let you define data processing pipelines—both batch and streaming—in a way that's portable across different execution engines.

Apache Beam architecture simplifies large-scale data processing by abstracting away the low-level details of distributed computing. You define your pipeline using an Apache Beam SDK in your preferred language, and then a component called a Runner translates and executes this pipeline on a compatible distributed processing backend (like Apache Flink, Apache Spark, or Google Cloud Dataflow). 

Here is a detailed breakdown of the key components of Apache Beam architecture and how they fit together to handle your data.

Beam's architecture revolves around four primary components:

➥ Pipelines — A pipeline represents the overall workflow of your data processing job. It defines the sequence of operations your data undergoes, from ingestion to transformation and output. You build pipelines using one of Beam's SDKs, such as Python, Java, or Go.

➥ PCollections — PCollections are the datasets manipulated within a pipeline. These can be bounded (finite sets) or unbounded (infinite streams). This abstraction allows you to work seamlessly with both batch and streaming data sources.

➥ Transforms — Transforms are operations applied to PCollections. They represent computations like filtering, mapping, grouping, or aggregating data. For example, you could use transforms to calculate metrics or clean raw input data.

➥ I/O Connectors — These are interfaces that allow pipelines to read from and write to external systems like databases, file systems, or message queues. Common connectors include integrations with Apache Kafka, Google BigQuery, and Amazon S3.

How Execution Works: Runners

Apache Beam decouples pipeline construction from execution by using runners. Runners translate your pipeline into native jobs that execute on distributed processing engines such as:

• Apache Flink
• Apache Spark
• Google Cloud Dataflow
• Apache Apex

This flexibility allows you to switch between execution environments without rewriting your pipeline code.

Unified Batch and Stream Processing

Unlike other frameworks that separate APIs for batch and streaming tasks, Apache Beam uses a single programming model for both paradigms. Because of this approach, it simplifies development and reduces overhead when switching between processing modes.

Portability API: Language-Agnostic Pipelines

Beam's Portability API is key to its flexibility. It decouples pipeline construction from execution by using protocol buffers and gRPC services. This allows you to:

• Define pipelines in your preferred programming language.
• Execute them in environments optimized for performance.
• Monitor job progress and reliability across systems.

API also supports Docker-based execution, which provides isolation and compatibility regardless of underlying infrastructure.

Apache Beam Features

Here are some key features of Apache Beam:

1) Unified Model — Apache Beam offers a single programming model for processing both batch (finite) and streaming (infinite) data. You define your pipeline logic once, and Apache Beam adapts it for either batch or stream processing.

2) Multiple Language SDKs — You can write Apache Beam pipelines using Software Development Kits (SDKs) for Java, Python, Go, and SQL. 

3) Portable Execution — Apache Beam pipelines are designed for portability. They can run on different distributed processing systems, known as runners. Popular runners are Apache Flink, Apache Spark, Google Cloud Dataflow, and others. This flexibility is one of the core key features of Apache Beam, allowing you to choose or switch execution environments without rewriting your pipeline.

4) Diverse I/O Connectors — Apache Beam provides a library of connectors (Sources and Sinks) to read from and write to numerous data storage systems. This includes systems like Apache Kafka, Google Cloud Storage, HDFS, BigQuery, and various databases.

5) Windowing for Streaming — For unbounded streaming data, Apache Beam has built-in support for windowing. This lets you divide the continuous data stream into logical, finite windows based on time or other characteristics, making stream processing manageable.

6) Extensibility — The Beam model is extensible. You can add support for new SDKs, runners, and I/O connectors.

Pros of Apache Beam:

• Apache Beam uses a single programming model for both batch and streaming data processing, which can simplify development and allow code reuse.
• You can write a pipeline once and run it on various execution engines (like Apache Spark, Apache Flink, or Google Cloud Dataflow) without major code changes.
• Apache Beam offers SDKs for Java, Python, Go, and SQL, letting teams use the languages they prefer.
• You can create custom connectors for data sources/sinks and new transformation libraries to meet specific needs.
• Apache Beam supports sophisticated event-time processing, windowing logic, and watermarks, which are useful for complex streaming scenarios.

Cons of Apache Beam:

• Abstraction that enables portability can sometimes introduce performance overhead compared to using an engine's native API directly
• Flexibility and abstraction can make Apache Beam complex to learn and set up, especially for mixed-language pipelines using the portability framework.
• SDKs for languages like C# or R are not available.
• Apache Beam might lag behind the native execution engines in supporting the newest features or optimizations available directly in platforms like Apache Spark or Apache Flink.
• Debugging can be trickier as you have both the Beam layer and the runner layer to consider.

5) Apache Spark Alternative 5—Dask

If you work primarily in the Python data science ecosystem (Pandas, NumPy, Scikit-learn) and need to scale beyond a single machine's memory or cores, Dask is a compelling option.

Dask is a Python library for parallel computing that scales data processing tasks from single machines to distributed clusters. It integrates with common tools like NumPy, pandas, and scikit-learn, allowing users to handle larger-than-memory datasets or parallelize existing workflows without rewriting code. Dask dynamically generates task graphs to manage computations efficiently, splitting work into smaller chunks and scheduling them across multiple cores or machines. It’s designed for flexibility, supporting both interactive analysis and production pipelines while maintaining compatibility with the broader Python ecosystem.

Dask simplifies scaling by mirroring familiar APIs—such as Dask DataFrame for pandas-like operations—and automatically adapting to available resources. Its distributed scheduler optimizes task execution, balances workloads, and recovers from failures, making it reliable for complex workflows like ETL, machine learning, or time-series analysis. Unlike rigid frameworks, Dask lets users incrementally scale workloads, avoiding overcommitment to specific infrastructures. It’s often paired with cloud storage or cluster managers but works just as well on a local machine, bridging the gap between small-scale prototyping and large-scale deployment.

Dask Architecture

Now, let's break down how its architecture works under the hood.

Core components

Dask operates through three key elements:

1. Client — Your entry point for submitting tasks. It analyzes your code to create a directed acyclic graph (DAG) of operations.
2. Scheduler — The brain that assigns tasks to workers and tracks progress. Unlike static systems, it dynamically adjusts task distribution as workers become available or fail.
3. Workers — Processes (local or cluster-based) that execute tasks and share intermediate results directly with each other.

When you call .compute(), the client converts your operations into a task graph. The scheduler then maps these tasks to workers while optimizing for data locality—keeping computations close to where data resides to minimize network traffic.

Task execution flow

The system uses a dynamic task scheduler that:

• Processes tasks in ~1ms intervals for rapid response
• Handles complex dependencies beyond simple map/reduce patterns
• Automatically re-routes failed tasks to healthy workers

Scaling mechanics

Dask achieves scalability through:

• Chunked data structures: Arrays/DataFrames split into partitions (e.g: 1,000-row Pandas chunks).
• Lazy evaluation: Builds full task graph before execution to optimize scheduling.
• Peer-to-peer communication: Workers exchange data directly without central bottlenecks.

Distributed execution

For cluster deployments:

• Workers can run across multiple machines
• Scheduler balances load using work-stealing algorithms
• TLS/SSL encryption secures inter-node communication

The architecture supports both single-machine multithreading and thousand-node clusters while maintaining compatibility with NumPy/Pandas APIs. You get parallel processing without rewriting existing code, though performance tuning requires understanding chunk sizes and task granularity.

Dask Features

1) Scalable Parallel Collections — Dask offers high-level collections—Array, DataFrame, and Bag—that mimic NumPy arrays, Pandas DataFrames, and Python lists. These collections split data into smaller chunks, allowing parallel operations on datasets that exceed memory limits.

2) Dynamic Task Scheduling — Dask builds task graphs to represent computation workflows and optimizes execution across cores or nodes. It uses lazy evaluation to delay computation until explicitly triggered, reducing unnecessary work.

3) Flexible Low-level APIs — The library provides Dask.delayed and Dask.futures, which let you convert ordinary Python functions into parallel tasks and run them asynchronously. This approach supports custom parallel workflows beyond standard collection operations.

4) Distributed Computing Support — Dask scales seamlessly from local multi-core environments to distributed clusters. It integrates with resource managers like Kubernetes, SLURM, and YARN, making it adaptable to various deployment scenarios. 

5) Real-time Monitoring Dashboard — A built-in web dashboard displays performance metrics, task progress, and resource usage in real-time, which helps track and optimize computations.

6) Integration with the PyData Ecosystem — Dask works with popular libraries such as NumPy, Pandas, scikit-learn, and Xarray. This compatibility lets you scale your existing code with minimal adjustments. 

7) Extensibility and Customization — Its modular design allows you to customize scheduling and performance tuning for specific workloads, making it adaptable to varied computational tasks.

Pros of Dask:

• Great fit if you're heavily invested in Python and libraries like Pandas/NumPy.
• Relatively easy transition for scaling existing Python code.
• Handles datasets larger than available RAM effectively.
• Flexible for parallelizing custom algorithms, not just DataFrame operations.
• Good visualization tools for understanding execution.
• Can be simpler to set up and manage than Apache Spark, especially on smaller scales.

Cons of Dask:

• Performance tuning (chunk sizes, task granularity) can require expertise.
• Resource management on large clusters can still be complex.
• For very small, quick tasks, the scheduling overhead might be noticeable.
• SQL capabilities on Dask DataFrames are less mature than Apache Spark SQL.
• Distributed deployment on Windows can be less straightforward than on Linux.
• It's primarily a Python solution; less ideal if your team uses Scala or Java extensively.

6) Apache Spark Alternative 6—Presto

Need to run fast SQL queries on data sitting in various places (Hadoop HDFS, S3, MySQL, Kafka, Cassandra) without moving it all into one central system first? That's what Presto is built for.

Presto is an open source, distributed SQL query engine initially developed at Facebook (now Meta) to address the performance limitations of Apache Hive for interactive queries on their massive data warehouse. It achieves speed by processing data in memory using a massively parallel processing (MPP) architecture.

a) PrestoDB (Now Presto): Originated at Facebook around 2012. Development continued under Meta's guidance for several years. In 2019, Meta contributed PrestoDB to the Linux Foundation to foster broader community involvement. It continues to be developed under the Presto Foundation, part of the LF. Notable initiatives include a native C++ execution engine effort and the Presto-on-Spark project for better batch workload handling.

b) Trino (Formerly PrestoSQL): In January 2019, the original creators of Presto (Martin Traverso, Dain Sundstrom, David Phillips, and Eric Hwang) forked the project due to differences in governance and vision, initially calling it PrestoSQL. They established the independent Presto Software Foundation (later renamed Trino Software Foundation). In December 2020, PrestoSQL was rebranded to Trino to avoid confusion. Trino has seen rapid development velocity, significant community adoption, and is often the focus of commercial offerings like Starburst.

While both projects share the same foundational architecture and goal of fast, federated SQL queries, they are evolving independently with different feature sets, optimizations, and community focus. When discussing "Presto" concepts, it's important to be aware of this split. Trino, in particular, has gained substantial momentum. The features described below generally apply to the core architecture shared by both, but specific advancements (like fault tolerance mechanisms) might differ.

Presto Architecture

Presto/Trino operates on the principle of separating compute (query processing) from storage (where data resides). It doesn't manage storage itself but connects to existing data sources. The architecture uses a coordinator-worker model:

Core Components

1) Coordinator — The “brain” of the operation. When you submit a SQL query, the coordinator:

• Receives SQL queries from clients (via CLI, JDBC/ODBC drivers, etc.).
• Parses, analyzes, and validates the SQL syntax.
• Consults connector metadata (schemas, table statistics) to create an optimized query execution plan.
• Schedules distributed tasks across available Worker nodes.
• Monitors the progress of tasks.
• Aggregates the final results from Workers and returns them to the client.
• Includes a Discovery Service for workers to register and send heartbeats.

2) Workers — The "muscle". These nodes handle the heavy lifting:

• Execute tasks assigned by the Coordinator.
• Use specific Connectors to fetch data directly from the underlying data sources.
• Process data primarily in-memory, utilizing pipelined execution between stages to minimize disk I/O.
• Transfer intermediate data between stages/workers as needed (via network exchange).

3) Connectors — Plugins that let Presto talk to different data sources. Each connector:

• Implements the Presto/Trino Service Provider Interface (SPI).
• Translates the specific protocols and data formats of the source system.
• Provides metadata (available schemas, tables, columns, data types, table statistics) to the Coordinator for query planning.
• Enables reading data (and sometimes writing data or managing tables, depending on the connector).
• Supported sources include: Hive (for HDFS, S3, GCS, etc.), Iceberg, Hudi, Delta Lake, MySQL, PostgreSQL, SQL Server, Oracle, Cassandra, MongoDB, Kafka, Elasticsearch, BigQuery, Redshift, Pinot, Druid, Kudu, Redis, Local Files, JMX, and many others.

Query Execution Flow

1) A client (e.g., CLI, JDBC/ODBC driver, BI tool) submits a SQL query to the Coordinator.

2) The Coordinator parses, analyzes, and optimizes the query, using metadata provided by the relevant Connectors.

3) A distributed Logical Plan is created, then optimized into a Physical Plan broken down into Stages. Stages represent phases of execution (e.g., scanning tables, joining data, aggregating results).

4) Stages are further divided into Tasks, which run in parallel on Worker nodes. Each task operates on one or more Splits (portions of the total data).

5) Workers execute tasks: They pull data Splits via Connectors, process data through a series of Operators (e.g., Scan, Filter, Project, Join, Aggregate) within the task.

6) Intermediate data is typically streamed (pipelined) between dependent tasks/stages across the network, staying in memory whenever possible to reduce latency. Data shuffling (exchange) occurs between stages when necessary (e.g: for joins or aggregations across partitions).

7) The final stage's results are gathered by the Coordinator.

8) The Coordinator streams the final results back to the client.

Presto architecture enables enables massively parallel processing (MPP) where multiple workers process different data splits simultaneously, guaranteeing high-speed, low-latency query execution.

Presto Key Features

Here are the key features of Presto:

1) Distributed Architecture — Presto uses a coordinator node to parse and plan queries and worker nodes to execute tasks.

2) Separation of Compute and Storage — Presto doesn't have its own storage system. It queries your data where it lives, whether that's in HDFS, cloud storage like Amazon S3, relational databases, or NoSQL systems.

3) In-Memory Query Execution — Data is processed in memory to minimize disk I/O, yielding rapid query responses.

4) ANSI SQL Support — Presto supports standard SQL queries, including complex operations like joins, aggregations, window functions, subqueries, and approximate percentiles.

5) Federated Queries — You can query data across multiple sources—relational databases (MySQL, PostgreSQL), non-relational systems (Cassandra, MongoDB), cloud storage (Amazon S3), and more—within a single query.

6) Scalability — You can scale Presto horizontally by adding more worker nodes to your cluster. More workers mean more parallelism and faster processing for large queries.

7) Extensibility — Presto's architecture uses pluggable connectors, allowing you or others to develop new connectors for additional data sources. You can also add user-defined functions.

8) Optimized for Interactive Queries — Presto is designed for low-latency, ad-hoc analytical queries, aiming to return results quickly so you can explore data interactively.

Pros of Presto:

• Fast, in‑memory query execution with low latency.
• Efficient scaling from small datasets to petabytes of data.
• Ability to query disparate data sources without data movement.
• Standard SQL support lowers the learning curve.
• Reduced storage overhead by eliminating data duplication.
• Seamless integration with existing data ecosystems via a wide array of Connectors.
• Real‑time insights enable faster decision-making and facilitate data preparation for machine learning tasks.

Cons of Presto:

• Lacks built‑in fault tolerance during query execution—if a query fails mid‑execution, it must be restarted manually.
• Resource‑intensive (especially memory) due to its in‑memory processing model.
• Not optimized for transactional (OLTP) workloads.
• Setup and tuning—particularly with Connectors and source‑specific optimizations—can be complex.

7) Apache Spark Alternative 7—Snowflake

Snowflake isn't a direct processing framework like Apache Spark or Apache Flink, but rather a fully managed, cloud-native data warehouse platform. It's become a popular alternative for companies moving their analytics workloads to the cloud, sometimes replacing systems where Apache Spark might have been used for ETL and querying within a data lake or traditional warehouse. Snowflake's key innovation is its architecture that separates storage, compute, and cloud services.

Snowflake Architecture

Snowflake uses a unique hybrid architecture combining elements of shared disk and shared nothing architectures. In the storage layer, data resides in centralized cloud storage accessible to all compute nodes, like a shared disk. However, the compute layer uses independent Virtual Warehouses that process queries in parallel, like a shared nothing architecture.

The Snowflake architecture has three layers:

• Storage Layer: Uses the cloud provider's object storage (S3, Azure Blob Storage, GCS) to store data efficiently (compressed, columnar format). Storage scales automatically.
• Compute Layer: Uses virtual warehouses (clusters of compute resources) to run queries. You can resize these warehouses instantly or have multiple warehouses of different sizes running concurrently against the same data, without impacting each other. Compute is independent of storage.
• Cloud Services Layer: The "brain" managing metadata, security, query optimization, transactions, etc.

You interact with Snowflake primarily using standard SQL. It handles infrastructure management, scaling, and tuning largely automatically.

Check out this comprehensive article to learn more about Snowflake's capabilities and architecture.

Snowflake Features

Here are some of the key features of Snowflake:

1) SQL Support — Snowflake uses standard SQL for querying data, making it familiar for those with SQL skills.

2) Concurrency — Snowflake can handle many concurrent users and queries efficiently.

3) Data Exchange — Snowflake provides access to a marketplace of data, data services, and applications, simplifying data acquisition and integration.

4) Secure — Snowflake has enterprise-grade security and compliance certifications. Data is also encrypted at rest and in transit.

5) Managed Service — Snowflake is fully managed with no infrastructure for users to maintain.

6) Web Interface — Snowflake provides Snowsight, an intuitive web user interface for creating charts, dashboards, data validation, and ad-hoc data analysis.

7) Time Travel — Snowflake allows you to query past states of your data using Time Travel. So you can do backfills or corrections to historical data up to 90 days.

8) Security Features — Snowflake provides IP whitelisting, various authentication methods, role-based access control, and strong encryption for data protection.

9) Auto-Resume, Auto-Suspend, Auto-Scale — Snowflake provides automated features for performance optimization, cost management, and scalability.

10) Snowflake Pricing — Snowflake uses a pay-for-usage model, resource optimization, flexible payment options and supports integration with cost-monitoring platforms (like Chaos Genius) for cost management.

—and much more!!

Pros of Snowflake:

• Excellent scalability and elasticity for both storage and compute.
• High performance for SQL-based analytics and BI workloads.
• Easy to use and manage; requires minimal administration.
• Pay-per-use pricing model can be cost-effective if managed well.
• Strong security and data recovery features.
• Good support for semi-structured data.
• Growing ecosystem and data marketplace.

Cons of Snowflake:

• Can become expensive if compute usage isn't carefully monitored and optimized.
• Primarily a SQL-based data warehouse; less suited for complex, non-SQL programmatic data transformations compared to Apache Spark/Apache Flink (though Snowpark allows Python/Java/Scala).
• Streaming ingestion (Snowpipe) is good but might not replace dedicated streaming platforms for all use cases.
• Limited support for unstructured data processing.
• Vendor lock-in to the Snowflake platform (though it runs on major clouds).

Apache Spark Alternatives—TL;DR: Summary of Key Features

Need a quick recap. Here's a table breaking down the 7 Apache Spark alternatives discussed in the above article.

Further Reading

• Apache Spark Documentation
• Apache Flink Documentation
• Apache Storm Official Site
• Apache Beam Documentation
• Dask Documentation
• Trino Documentation
• Snowflake Documentation
• Databricks vs Snowflake

Conclusion

And that’s a wrap! As you can see, Apache Spark's got some serious competition. Apache Storm brings blazing speed to real-time stream processing. Apache Flink's got advanced stateful capabilities that set it apart. Hadoop's the foundation for batch processing, while Apache Beam promises flexibility with its write-once-run-anywhere approach. Dask seamlessly scales Python, Presto's a master of federated SQL, and Snowflake's cloud-native warehousing is incredibly agile. The "best" Apache Spark alternative really depends on your specific challenges, team expertise, and performance needs. Weigh the pros and cons, maybe run a proof-of-concept, and pick the tool that gets your job done best.

In this article, we have covered:

• What is Apache Spark and what is it used for?
• What are the Limitations of Apache Spark?
• 7 Popular Apache Spark Alternatives
  • Apache Spark Alternative 1—Apache Storm
  • Apache Spark Alternative 2—Apache Flink
  • Apache Spark Alternative 3—Apache Hadoop
  • Apache Spark Alternative 4—Apache Beam
  • Apache Spark Alternative 5—Dask
  • Apache Spark Alternative 6—Presto
  • Apache Spark Alternative 7—Snowflake

… and so much more!

FAQs

Which is better than Spark?

No single tool is universally "better". It depends entirely on the job. Apache Flink often beats Apache Spark for low-latency stateful streaming. Presto/Trino is often faster for interactive federated SQL queries. Dask can be better for scaling Python-native code. Snowflake offers a managed platform experience….Spark (as a framework) doesn't. Pick based on your specific needs.

Is Apache Spark still relevant?

Yes. Absolutely. Apache Spark remains a dominant force in big data, particularly for large-scale batch processing, ETL, and as a unified engine for various tasks including ML. Its ecosystem is huge, and development still continues.

Which is better, Spark or Kafka?

They do different things. Kafka is a distributed event streaming platform (a durable message queue). It's excellent for ingesting and storing streams of data reliably. Spark (specifically Spark Streaming/Structured Streaming) is a processing engine that can read from Kafka, process the data, and write results elsewhere. They are often used together: Kafka ingests, Spark processes.

Is Apache Spark still a good choice for large–scale data processing?

Yes, especially for batch processing, large ETL jobs, and machine learning pipelines where its in-memory speed and unified libraries shine. However, for requirements like true real-time processing (millisecond latency) or purely interactive SQL across many sources, Apache Spark alternatives might be a better fit.

Why is Apache Flink sometimes considered better than Spark for streaming?

Apache Flink was built as a true stream processor from the start. This gives it advantages in:

• Lower Latency: Processes event-by-event, not in micro-batches.
• State Management: More mature and flexible built-in state mechanisms designed for streams.
• Event Time Processing: Robust native support for handling events based on when they occurred.

Spark's Structured Streaming is powerful but based on a micro-batch paradigm, which inherently adds some latency and influences how state and time are handled, although recent Spark developments are closing the gap.

Is Flink still relevant?

Yes, very much so. Apache Flink is a leading engine for demanding real-time stream processing applications requiring low latency and sophisticated state management. Its adoption continues to grow.

Can Apache Beam replace Spark?

No, not directly. Apache Beam is a programming model and SDK, while Spark is an execution engine. You can write a Beam pipeline and choose Spark as the runner to execute it. Apache Beam provides portability across engines like Apache Spark, Apache Flink, etc., but it relies on them to actually run the job.

Is Hadoop an Apache Spark alternative?

No. Not really, in terms of processing. Spark largely replaced Hadoop MapReduce as the preferred processing engine due to its speed. However, Hadoop's storage layer (HDFS) and resource manager (YARN) are still commonly used together with Spark. So, Spark often runs within a Hadoop ecosystem, but replaces the MapReduce component.