Companies are handling a large amount of data every moment. They also want insights into big data. This is not possible without dedicated ecosystems capable of handling such massive data. Many frameworks are available for big data processing, such as Hadoop, Hive, Apache Spark, MapReduce, and more. Learning at least one such framework is critical for a career in big data analytics, data engineering, or machine learning. So, how do you know which framework to learn?
As per benchmarks, Apache Spark is more popular because it is cost-effective to deploy. However, there are fewer Spark experts in the job market. Comparatively, MapReduce professionals are more readily available. So if you have decided to go for big data analytics training or a career in data engineering, opt for an Apache Spark Certification as the demand is high, and it will increase your chances of career growth.
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What is Apache Spark
Apache Spark is an open-source framework for processing big data workloads. Its distributed processing system executes data engineering, data science, and machine learning tasks for fast queries and batch processing for advanced analytics on the Apache Hadoop platform. It processes in-memory and processes multiple workloads in parallel. The in-memory caching speeds up algorithms that call a function on a dataset. Reuse of data through DataFrames lowers the latency, making Spark a fast processing engine.
The platform supports easy-to-use native APIs in Python, Java, Scala, and R for a developer-friendly and flexible environment. As a framework for large-scale batch processing and stream processing, MapReduce can process large data sets at high speed while distributing tasks across multiple machines. All while enabling code reuse in batch processing, graph processing, real-time streaming analytics, and interactive queries for high performance.
Why take a Spark Certification
As a fast and general-purpose cluster computing system, Spark powers large-scale data processing and analytics tasks. Its immense capabilities are world players such as Netflix, Conviva, Alibaba, Yahoo, Apple, Google, and Facebook are adopting Spark. Spark has a wide range of applications across multiple industries.
Some instances are:
- Banking: Spark can detect fraudulent transactions, analyze customer spending for recommendations, and execute pattern identification for investments.
- Healthcare: From analyzing patient data to the identification of potential health issues and diagnosis based on medical history, Spark has found many uses.
- Entertainment: Entertainment companies like movie and video streaming engines use Spark for featuring relevant ads and recommendations based on user behavior. It also helps to offer a better user experience by eliminating screen buffering.
- E-Commerce: Large data sets can be analyzed for real-time transaction details, customer browsing history, cart abandonments, etc., for customized recommendations.
- Telecom: Telecom operators use Spark to process customer data for analyzing the optimal mix of products and minimize the tariff for the bundle of products in usage.
The wide deployment of Spark across industries and the many innovative use cases make for a strong case for an Apache Spark Certification. Certification offers you the perfect opportunity to learn a leading big data and analytics platform in high demand and boost your employability.
What is MapReduce
MapReduce is a Java-based framework within the Hadoop ecosystem. It is Hadoop’s native data processing engine. Its distributed component mitigates the complexity of distributed programming with two steps to the process: Map and Reduce. While Map splits the data across parallel processing jobs, Reduce handles the aggregate data from the Map step. MapReduce uses the Hadoop Distributed File System (HDFS) for input and output and supports the building of select technologies on top of it. As a software framework, it allows applications to be written for the processing of data on large clusters of commodity hardware running in parallel. This enables scalability with hundreds and more servers in a Hadoop cluster.
Differences and Similarities between MapReduce and Spark
MapReduce differs from Spark in that it processes data on disk, whereas Spark processes and retains data in memory for ensuing steps. Consequently, for smaller workloads, Spark’s processing speed is 100x faster than that of MapReduce.
MapReduce is used for writing data into the HDFS, whereas the Spark framework is used for faster data processing. While MapReduce cannot handle real-time processing, Spark can do it with ease. Apache Spark can cache the memory data for processing, which MapReduce cannot.
However, both frameworks also have some similarities. Both are open-source frameworks for scalable distributed computing. Spark’s compatibility with various data types and data sources is similar to MapReduce.
So let us explore how MapReduce and Spark compare:
1. Performance
MapReduce persists data back to the disk after a map or reduce action, but Apache Spark is faster as it processes data in RAM.
However, Spark requires a lot of memory as it loads a process into memory and caches it till called. So, while processing large data that does not fit in the memory, Spark may suffer major performance setbacks. But MapReduce stores data on multiple sources, processing it in batches for smoother performance. It kills the processes right after completing the task enabling it to run together with other services with minimal performance issues.
MapReduce excels in ETL-type jobs, and Spark supports iterative computations.
Summing up:
Spark is the better choice where all data fits into memory and dedicated clusters. MapReduce is the alternative for when data does not fit into memory.
2. Processing
MapReduce allows parallel processing of large data and outperforms Spark, where the resultant dataset is larger than available RAM. However, for iterative processing and graph processing or real-time processing, Spark out does MapReduce. Spark has a built-in machine learning library, while MapReduce needs a third party for machine learning tasks.
Summing up:
Spark is the choice for real-time processing and live unstructured data streams, while MapReduce works well for batch processing and linear data processing.
3. Scalability
MapReduce scales up quickly to accommodate an increase in demand via the HDFS. Spark relies on fault-tolerant HDFS for processing large volumes of data.
Summing up:
For rapidly growing data volume, MapReduce is the choice for its high scalability as you can add up to n different nodes.
4. Security
In terms of security, MapReduce ranks higher. It applies multiple authentication and access control methods making MapReduce more secure. Spark is less advanced when compared with MapReduce. In Spark, security is set to “off” by default and makes it vulnerable to attack. Authentication via a shared secret or event logging is the security consideration. Summing up:
MapReduce is the better option when security considerations are critical.
5. Failure Tolerance
MapReduce relies on hard drives rather than RAM. So if a process crashes during execution, it can resume the processing where it left off. Whereas, in Spark, the processing must restart from the very beginning.
Summing up:
MapReduce is somewhat better in fault tolerance than Spark.
6. Cost
MapReduce uses any disk storage type for data processing, but Spark uses high RAM to spin up nodes as it depends upon in-memory computations for real-time data processing.
Summing up:
For processing very large quantities of data, MapReduce is cheaper because hard disk space is less expensive than memory space.
7. Ease of Use
Although MapReduce is in Java, programming is difficult as code is required for each process. Also, it lacks an interactive mode. Spark has pre-built rich APIs for Java, Scala, and Python, and also has the Spark SQL for the SQL savvy. The simple building blocks make it easy to write user-defined functions.
Summing up:
Although Spark is easier to program, MapReduce has several tools that simplify programming.
Summary
Both frameworks are driven by the enterprise goals of faster, scalable, and more dependable data processing. Ultimately, the choice of the big data technology stack must consider the pros and cons of Spark and MapReduce.
Apache Spark is generally the tool of choice for data scientists and analysts. However, it depends upon the quantity of data processed by the framework. Processing big data requires careful consideration before choosing the best option. Both frameworks have features that the other does not have. So one must factor which framework best fits the data analytics needs before settling upon the framework and mastering it.
