When to Choose Kafka vs. Hadoop
Before we can compare Kafka vs. Hadoop, it's important to understand their commonalities as key players in the big data space. Kafka and Hadoop are enterprise-grade open source projects overseen by the Apache Foundation, and they're both well-adopted technologies that have been around for more than a decade. This patronage and longevity has matured them, making their base functionality reliable and robust. It has also brought them a lot of attention and contributions, resulting in each of them becoming established ecosystems.
In this blog, we'll explore why teams might select Kafka vs. Hadoop for data management infrastructure as part of their big data strategy.
- Why Compare Kafka vs. Hadoop?
- Kafka vs. Hadoop: Key Differences
- How Kafka and Hadoop Fit Into the Big Data Landscape
- Final Thoughts
Why Compare Kafka vs. Hadoop?
Although the original motivation and approach of these projects were distinctly different, the wide range of complimentary software, add-ons, integrations, and evolving features have created overlap in the use cases these technologies can target. However, make no mistake, Kafka and Hadoop still have distinct areas of strength.
While Kafka and Hadoop compete for attention in the big data space, they are not 1:1 competing solutions, which is why it is important to reduce the noise and learn the core mission of each. Successful implementors understand where Kafka vs. Hadoop can stretch and when it makes sense implement both.
What Is Apache Kafka?
Apache Kafka is a distributed event streaming system designed to ingest and analyze real-time data feeds that generally have no defined start or end point. It is ideal for helping to manage time series data — situations where the data is generated continuously.
At the core, Kafka is a messaging queue, a place where applications that share data (producers) can publish information (messages), and applications that need to read and heed that information (consumers) can subscribe to obtain that data as it arrives. It has corresponding APIs for these purposes, as well as a "Connector API" that serves as a convenience wrapper around the Producer API and Consumer API for existing applications to create integrations with Kafka that facilitate data streaming exchanges.
That said, from the beginning, Kafka's design was optimized for handling analysis of enormous real-time data feeds through the addition of the Streams API and Processor API. The Streams API has standard functions for configuring common data analysis processes, like filtering, mapping, grouping, windowing, aggregating, and joining data. The Processor API, on the other hand, provides for low-level development of custom analysis functions.
With this, Kafka is able to ingest, analyze, store, and share its findings very quickly.
What Is Apache Hadoop?
Apache Hadoop is a framework that allows for distributed processing of large data sets across clusters of computers. It is designed to scale horizontally from single servers to thousands of nodes, each offering local computation and storage.
Although Hadoop will run on any hardware, it was designed to run on commodity hardware. It was developed from the ground up to expect hardware failures and to recognize and handle them gracefully. As the size of the data grows, additional servers can be added to store and analyze the data at a low cost.
Hadoop is made up of essentially four parts; however, for simplicity, there are two key components of interest:
- Hadoop Distributed File System (HDFS), a distributed storage system that marshals incoming data and distributes it across servers in the Hadoop clusters. Each server stores only part of the full data set to optimize IO. Each part of the data set is also duplicated and stored on more than one server to achieve fault tolerance.
- MapReduce, a distributed data processing framework that schedules and distributes the execution of code to the server where the data resides or as close as possible to reduce network traffic. It then collects and aggregates the results to achieve the final output of each computational phase.
These components enable Hadoop is able to parallelize problems and analyze very large datasets faster.
Kafka vs. Hadoop: Key Differences
Kafka was designed for processing vast amounts of data in real-time. It expects data to be continuous, so it uses the Streams API to process data as it comes in. It applies filters, mappings, groupings, and aggregations to segments of the data. It does this in memory when possible for speed, but it also provides the ability to work beyond the available memory by using the producer and consumer APIs internally to capture and persist the results.
Kafka is best suited to handle large volumes of data in motion, providing alerts or triggering immediate actions based on what is observed via Kafka Streams.
Hadoop was designed for processing large datasets in batch. It expects all the applicable data to have been ingested and stored in HDFS before scheduling iterations of jobs that apply the MapReduce engine to divide and conquer the analysis steps. It is optimized to process a known set of data all at once. This processing could take minutes, hours, or even days to complete.
Hadoop is best suited to mine enormous volumes of data at rest, identifying meaningful trends or anomalies that can be used to further refine the analysis or make future decisions.
How Kafka and Hadoop Fit Into the Big Data Landscape
Both Kafka and Hadoop are enterprise grade big data systems that provide highly available, reliable, and resilient data processing and storage features at scale.
They are also both very extensible, allowing developers to easily add specialized and custom features, add-ons, other integrations. The ecosystem around each of these products is very strong and growing. In fact, there are partner and complimentary products available that can help solve many of the shortcomings that either Kafka or Hadoop have as part of their native feature set.
However, as mentioned previously, they each have unique strengths where they can be considered best in class for solving specific problems, particularly in the finance, defense, and healthcare sectors.
Kafka Use Cases
- Finance: Kafka is used to ingest market data, including stock prices, trading volumes, and other relevant financials from various exchanges. Traders, trading systems, and financial analysts can access this data instantaneously and make informed decisions based on current market information.
- Defense: Kafka is used for real-time data ingestion from sensors, satellites, drones, and other surveillance devices. It disseminates this data to support real-time situational awareness and command-control systems.
- Healthcare: Kafka is used to stream patient data from various sources, like medical devices, electronic health records, and monitoring systems. Then, Kafka is used to facilitate the distribution of the data to different downstream applications and systems, sometimes doing some level of filtering or analysis to enrich the data via Kafka Streams. This ultimately enables medical providers to access current patient information and make timely treatment decisions.
Hadoop Use Cases
- Finance: Hadoop is utilized to store and analyze historical market data. It handles vast amounts of past stock price points, market sentiment analysis, and other financial indicators. Analysts then run batch processing jobs on Hadoop to determine historical trends, correlations, and statistical metrics, such as moving averages, volatility, and trading volumes. This analysis can help traders and portfolio managers make long-term investment decisions and identify patterns in historical stock performance.
- Defense: Hadoop is used for intelligence analysis, cybersecurity, and threat detection. It processes vast amounts of historical data, such as cyber threat logs, signals intelligence, and geospatial data. It enables defense analysts to perform diagnostics, build predictive models, and identify patterns that help understand and counter potential threats.
- Healthcare: Hadoop processes and analyzes large-scale healthcare data, such as medical records, clinical trial results, genomic data, and population health data. Research organizations and healthcare providers can use Hadoop to gain insights into disease patterns, patient outcomes, and treatment effectiveness.
Kafka excels at real-time event streaming and data integration, while Hadoop is best at large-scale batch processing and historical data analysis. A mature and robust big data implementation would likely use both Kafka and Hadoop, leveraging each technology's core strengths and vast ecosystem.
Here's an example of what that might look like: Data could be harvested and ingested into Hadoop via Kafka, analyzing defined windows of data in real-time via Kafka Streams. The real-time processing would identify micro-trends and anomalies in the data, providing real-time alerts that trigger actions and inform future configurations.
The outcomes of the Hadoop analysis could further tune the Kafka Streams processing to help identify key decision points and significant events within new data that is streaming in real-time. Ultimately, Kafka could distribute insights to downstream applications, some immediately and others after analysis and refinement.
So in some cases, it's not a question of Kafka vs. Hadoop, but Kafka and Hadoop. Hopefully this blog has provided insight into how both Kafka and Hadoop have an important place in the landscape of open source data technologies.
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