Big Data & Processing Frameworks

Big data and streaming frameworks enable processing massive datasets and real-time data streams at scale, forming the backbone of modern data engineering and analytics infrastructure. Apache Spark dominates distributed processing with PySpark appearing in >15% of Data Engineering positions, providing unified batch and streaming capabilities. Hadoop maintains presence (>15% in data engineering) as foundational HDFS and MapReduce infrastructure, though declining relative to Spark. Kafka has emerged as the universal streaming platform, appearing in >75% of Real-time & Streaming Systems roles, >75% of Asynchronous Messaging Systems positions, and >10% of Data Engineering roles, enabling real-time data pipelines and event-driven architectures. Flink serves specialized low-latency stream processing (>5% prevalence). The landscape shows convergence around Spark for batch analytics and Kafka for streaming, with these technologies appearing across data engineering, ML operations, and real-time backend engineering. Entry-level accessibility is moderate for foundational tools like Hadoop (>20% in entry-level data engineering), PySpark (>10%), and Kafka (>15%), though streaming expertise typically requires more experience. Mastery of these frameworks is essential for data-intensive career paths.

Batch Processing Frameworks

Distributed computing frameworks for large-scale batch data processing and analytics. Apache Spark leads with PySpark as the primary interface for data engineers, while Hadoop provides foundational distributed storage and processing. These frameworks are central to data engineering with moderate entry-level accessibility.

PySpark

Moderate Demand

Rank: #1

Entry-Level: Moderate

Python API for Apache Spark in Data Engineering (>15%) and big data processing contexts. Moderate entry-level demand with >10% prevalence. Python interface to Spark. Used for large-scale data processing with Python, distributed ETL pipelines, big data analytics, machine learning on big data with MLlib, joining and aggregating massive datasets, and data engineering workflows leveraging Python ecosystem with Spark's distributed computing.

Apache Spark

Moderate Demand

Rank: #2

Entry-Level: Low

Unified analytics engine in Data Engineering (>10%), Real-time & Streaming Systems, and big data contexts. Lower entry-level accessibility. Fast distributed processing. Used for batch and streaming data processing, in-memory computing for speed, SQL queries on big data, machine learning pipelines, graph processing, supporting Scala/Java/Python/R, and replacing MapReduce for faster big data analytics.

Hadoop

Moderate Demand

Rank: #3

Entry-Level: Moderate

Distributed storage and processing framework in Data Engineering (>15%) and big data ecosystems. Moderate entry-level presence with >20% prevalence. Foundational big data technology. Used for HDFS distributed file storage, MapReduce processing, data lake infrastructure, batch processing large datasets, enterprise data warehousing foundations, and supporting Spark, Hive, and other big data tools in Hadoop ecosystem.

Stream Processing Frameworks

Real-time data processing frameworks for continuous data streams and event processing. Flink specializes in low-latency stateful stream processing, while Storm represents older generation streaming. These tools serve specialized real-time analytics and processing needs with limited entry-level accessibility.

Flink

Moderate Demand

Rank: #1

Entry-Level: Low

Stream processing framework in Data Engineering (>5%), Real-time & Streaming Systems (>5%), and real-time analytics contexts. Limited entry-level opportunities. Stateful stream processing. Used for real-time analytics on streaming data, complex event processing, low-latency data pipelines, exactly-once processing guarantees, event-time processing with watermarks, and applications requiring sub-second latency with high throughput on continuous data streams.

Storm

Low Demand

Rank: #2

Entry-Level: Low

Real-time computation system with declining presence in modern architectures (<5% overall prevalence). Rare entry-level demand. Legacy streaming technology. Used for maintaining legacy real-time processing systems, continuous computation on streaming data, distributed RPC, and organizations with existing Storm investments transitioning to modern alternatives like Flink or Spark Streaming.

Message Streaming Platforms

Distributed streaming platforms for publishing, subscribing, and processing real-time data streams. Kafka dominates as the universal event streaming platform, appearing across data engineering, backend streaming, and async messaging roles. Kafka Streams and Spark Streaming extend stream processing capabilities. Strong to moderate entry-level opportunities for core Kafka.

Kafka

Very High Demand

Rank: #1

Entry-Level: Moderate

Distributed event streaming platform in Real-time & Streaming Systems (>75%), Asynchronous Messaging Systems (>75%), Data Engineering (>10%), Microservices Architecture (>10%), Systems Integration (>5%), MLOps (>5%), and Backend Testing & QA (>5%). Moderate entry-level demand with >15% in relevant roles. High-throughput messaging. Used for real-time data pipelines, event streaming, log aggregation, microservices communication, change data capture, building event-driven architectures, and serving as central nervous system for real-time data infrastructure.

Apache Kafka

Moderate Demand

Rank: #2

Entry-Level: Low

Full Apache project name for Kafka in Real-time & Streaming Systems (>5%) and Asynchronous Messaging Systems (>5%). Often listed alongside or instead of just 'Kafka'. Same use cases as Kafka: distributed event streaming, real-time data pipelines, publish-subscribe messaging at scale, durable message storage, and event sourcing architectures.

Kafka Streams

Low Demand

Rank: #3

Entry-Level: Low

Stream processing library built on Kafka with limited explicit presence (<5% overall prevalence). Requires Kafka expertise first. Client-side stream processing. Used for real-time transformations on Kafka topics, stateful stream processing within Kafka ecosystem, building streaming applications without separate cluster, exactly-once processing semantics, and lightweight stream processing without Spark or Flink overhead.

Spark Streaming

Low Demand

Rank: #4

Entry-Level: Low

Spark's streaming module with limited explicit mention (<5% prevalence). Part of broader Spark ecosystem. Micro-batch streaming. Used for real-time data processing with Spark, integrating streaming and batch in unified codebase, processing Kafka streams with Spark, machine learning on streaming data, and organizations already using Spark wanting to add streaming capabilities.