The digital transformation of businesses has accelerated the implementation of Big Data technologies for business growth. Companies ingest complex data in huge volumes, which traditional software applications cannot handle. Big Data technologies support software utilities to process the massive data streams in real-time and extract insights for predictive models and on-demand analytics.
To carve a future in a Big Data domain, you must master the relevant technologies. While several platforms are available as open-source, taking out time for an Apache Spark Online Course Free of cost can be a great way to start your journey in Big Data technologies.
A Primer on Big Data Technologies Ruling the World
The choice of Big Data technologies and frameworks depends upon the company’s needs, resources, the kind of data they handle, and processing speeds. Data storage, computing power, and hardware considerations are the other factors.
Most of the trending Big Data frameworks are open source. Some examples are Apache Hadoop, Apache Spark, Apache Hive, and Apache Kafka, with various supporting tools.
The Hadoop Architecture
With companies looking for high-performance technologies to tackle Big Data problems, the distributed computing architecture of Hadoop and its built-in powerful computing have made it a popular go-to platform.
The following benefits have endeared Hadoop to the Big Data user:
- The Hadoop technology stack has a framework that supports distributed storage and Big Data processing with distributed workloads across multiple low-cost commodity servers.
- It is scalable, resilient to failure, and very flexible for processing huge unstructured data running as nodes in a Hadoop cluster.
- Its Hadoop Distributed File System (HDFS) and code distribution/execution feature, the MapReduce, provides a batch engine.
However, the following downsides of Hadoop have given rise to an alternative to the Hadoop technology stack:
- The Hadoop ETL does not support data pipelining.
- Lacks interactive mode.
- Hadoop has poor performance speed as it reads and writes from a disk and cannot cache data in memory.
- With no iterative capability, it requires external machine learning tools and job schedulers for complex workflows.
- Hadoop processing is limited to batch processing of historical data
- Its 1,20,000 lines of lengthy codes take time to execute.
- Hadoop is unable to provide analytics in real-time.
The Apache Spark Ecosystem
Apache Spark is also an open-source processing framework built for removing the downsides of Hadoop.
Its benefits over and above Hadoop include:
- With only 20,000 lines of code, Spark runs faster.
- As the number of read-write to disk tasks is reduced, applications run nearly 100x faster in memory and 10x faster on the disk.
- It supports data processing from multiple data sources and integrates into a single workflow. Spark’s distributed stream and batch data processing support auto-scalability for computing and analysis of huge data volumes.
- Spark can process even petabytes of data concurrently together with real-time data, in parallel, storing data in the distributed cluster memory for high processing speeds.
- Its in-memory iterative computing supports faster analytics.
- As Apache Spark can tackle a variety of workloads – batch, interactive, iterative, and streaming.
- Data storage in memory reduces read-write cycles, making it a high-speed Big Data framework for running applications up to 100x faster in memory and 10x faster on disk than Hadoop.
- Spark supports fast, streaming analytics in real-time and is the preferred data analytics engine for Data Science tasks.
- It also has its machine learning library for iterative learning.
- The in-memory computing facilitates the storage and processing of Big Data in real-time.
Another differentiator is that Spark can run independently on top of existing Hadoop clusters using YARN or MapReduce.
However, the downsides are low security, as it has only a single authentication. Spark is also more expensive for deployment because it uses massive RAM and more clusters.
Hadoop or Apache Spark: What to learn today
With more and more companies crunching Big Data across industry scenarios and applications. With both, Hadoop and Apache Spark having their own capabilities, the ultimate choice of a suitable framework boils down to the challenges of Computing Speed, Data Mining, Storage, Analytics, and Machine Learning support. Both frameworks support tools that accommodate only specific businesses. For instance, for the computation of huge data streams from IoT networks in real-time, Apache Spark is considered the most suitable. Spark has also done away with the many disadvantages of Hadoop, thus emerging as the popular go-to big data tool for Big Data processing and analytics.
With more and more companies crunching Big Data across industry scenarios and applications, they are deploying both Hadoop and Apache Spark for business growth. However, the ultimate choice of a suitable framework boils down to the challenges of Computing Speed, Data Mining, Storage, Analytics, and Machine Learning support.
These two frameworks support tools that accommodate only specific businesses. For instance, the computation of data streams from IoT networks in real-time, calls for Apache Spark. Spark has also done away with the many disadvantages of Hadoop, thus emerging as the popular go-to Big Data tool for Big Data processing and analytics.
Companies also factor in cost, performance, security, and ease of use when deploying the Big Data framework. Because of the high performance and computing speeds, Apache Spark is the platform of choice. But, where cost considerations rule, Hadoop has a higher rating since it relies on on on-disk storage for data processing. The costs of running applications are also lower than in Spark, which uses plenty of RAM and clusters.
The processing needs of companies differ as well. Where real-time in-memory batch processing is the priority, Spark excels. However, where the goal is to store data on disks and analyze it in parallel in batches across a distributed environment, Hadoop is best suited for such linear data processing.
Hadoop is also used where time is no time limits, and data processing is from historical data. Any tasks that require graph-parallel processing, real-time streaming data analysis, and ML applications prefer Spark.
What technology to master today would largely depend upon your knowledge of Bog Data tools implemented with these frameworks and the companies where you aim to make your career. For instance, if you plan to apply for a relevant job role in companies such as Uber, Pinterest, Netflix, Twitter, Spotify, AWS, Cloudera, IBM, and Microsoft, you must learn about Hadoop. And if you aim for top players like Oracle, Cisco, Hortonworks, Verizon, Amazon, eBay, TripAdvisor, Netflix, Yahoo, Roche, Alibaba, and Pinterest, then make Apache Spark part of your Big Data learning curve.
Over the past few years, Apache has released more than 50 related software systems and components that can run with the Hadoop ecosystem. Vendors have packaged UIs and extensions with enterprise-level support for Big Data environments. It has also emerged as a redeeming feature for deploying Hadoop vs Apache Spark.
Conclusion
The popularity of Hadoop and Spark has driven a steep demand for Big Data skills. Knowledge of Java, MapReduce, and YARN are in high demand in the job market for those who want to master Hadoop. And those with Data Engineering skills, Scala programming, MLlib knowledge, GraphX computation, and Spark SQL, may like to master Apache Spark. Ultimately, it depends upon your learning interests and the effort you put into the Big Data technology framework.
