“There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.”
~ Eric Schmidt, Executive Chairman at Google
With the famous quote above, we can understand that organizations have grown increasingly reliant on real-time insights and quick decision-making in the current era, which is characterized by a fast pace and data-centric operations. As a result, data streaming has become increasingly important, acting as a key piece of technology that enables real-time or nearly real-time data processing and analysis.
According to a recent report by IBM Marketing Cloud, “90 percent of the data in the world today has been created in the last two years alone, creating 2.5 quintillion bytes of data every day — and with new devices, sensors and technologies emerging, the data growth rate will likely accelerate even more”.
Technically speaking, this means that the world of big data processing will get more complicated and difficult. In response to the growing demand for real-time insights and prompt decision-making in the face of rising data volume, velocity, and complexity, distributed stream processing of data was developed.
In response to the growing need for distributed stream processing of data, numerous open-source streaming frameworks have emerged. Some of the popular Data streaming frameworks are, Apache Kafka, Apache Flink, Apache Spark Streaming, Apache Storm, Apache Samza, Heron (developed by Twitter), Amazon Kinesis, Apache NiFi, RabbitMQ.
Despite the prevalence of competing data streaming frameworks in the big data space, Apache Spark regularly tops the field when it comes to creating scalable, quick data pipelines. Let’s explore the reasons behind its continued dominance in this field.
FYI: Data streaming is the live flow of information, capturing and processing data in real-time for instant insights and actions. It’s like riding a wave of data, staying in the groove of the continuous stream.
What’s the secret behind Apache Spark’s blazing-fast data streaming?
When it comes to streaming massive volumes of data at breakneck speeds, Apache Spark is a slick player. It functions according to the distributed processing principle, which divides the workload into smaller jobs that run concurrently across a cluster of computers. Spark keeps the party going with its resilient and fault-tolerant design by processing data in micro-batches, enabling almost real-time processing and analysis. Spark is a heavyweight in the field of high-speed data streaming because it is all about moving to the rhythm of speed and scalability.
Have you heard?!! “Spark founders state that an average time of processing each micro-batch takes only 0.5 seconds.”
Let’s take a glance at how Apache Spark architecture works
Source: Click here
Source: Click here
Apache Spark comprises several key components that work together seamlessly. A cluster manager, like Apache Mesos or Apache YARN, handles resource allocation and cluster management. Spark interacts with distributed file systems, such as Hadoop Distributed File System (HDFS) or Amazon S3, for data storage and fault tolerance. The driver program serves as the entry point for users, defining the computation logic and coordinating task execution. Resilient Distributed Datasets (RDDs) form the core data structure in Spark, enabling distributed data processing across the cluster. The DAG scheduler optimizes task execution by analyzing dependencies and generating an optimal execution plan. Spark divides the work into tasks and assigns them to worker nodes for parallel execution. The architecture incorporates fault tolerance mechanisms to handle failures gracefully. Additionally, Spark’s architecture supports various libraries and extensions like Spark SQL, Spark Streaming, MLlib, and GraphX, which expand its capabilities for handling structured data, streaming, machine learning, and graph processing. Overall, Apache Spark’s architecture enables efficient, scalable, and fault-tolerant processing of large data sets across distributed clusters.
FYI:
- Apache Spark uses Machine Learning-enabled Data Analytics modules and supports batch and stream data processing.
- The platform can process enormous volumes of complicated data at rapid speeds and performs workloads 100x quicker than Hadoop.
- Apache Spark comes pre-configured with an easy-to-use API that simplifies Big Data processing and distributed computing for developers.
What are the practical areas where Apache Spark excels?
Let’s take a look at some of the industries where Apache Spark has made a remarkable impact and proven to be highly beneficial:
Source: Click here
Financial Services: Apache Spark is widely used in the financial sector for tasks such as fraud detection, risk analysis, algorithmic trading, portfolio optimization, and customer analytics. Its ability to handle large volumes of data and perform complex computations at scale makes it valuable in this industry.
Healthcare: In the healthcare industry, Apache Spark finds applications in areas such as genomics research, personalized medicine, medical imaging analysis, disease prediction, and healthcare analytics. Spark’s ability to process and analyze large volumes of medical data quickly contributes to advancements in patient care and research.
“In the proposed scalable system, medical parameters are sent to Apache Spark to extract attributes from data and apply the proposed machine learning algorithm. In this way, healthcare risks can be predicted and sent as alerts and recommendations to users and healthcare providers” – ResearchGate
E-commerce and Retail: Apache Spark is utilized in e-commerce and retail industries for various purposes, including recommendation systems, customer segmentation, demand forecasting, inventory management, and personalized marketing. Spark’s machine learning and real-time processing capabilities are particularly valuable in these domains.
Telecommunications: Telecommunication companies leverage Apache Spark for tasks like network optimization, call detail record (CDR) analysis, customer churn prediction, network traffic analysis, and fraud detection. Spark’s distributed processing capabilities enable telecom operators to handle and analyze vast amounts of network data efficiently.
Final Thoughts:
In summary, Apache Spark is a powerful solution that effectively tackles the demand for fast and scalable data processing in today’s world. It excels in performance, scalability, fault tolerance, versatility, and seamless integration with the big data ecosystem. Various industries dealing with large volumes of data can harness the complete capabilities of Apache Spark to construct reliable and efficient data pipelines.
Further, Spark simplifies data preparation on a large scale across multiple sources, effectively combining data and AI. It offers a cohesive set of APIs that cater to both data engineering and data science tasks, while seamlessly integrating well-known libraries like TensorFlow, PyTorch, R, and SciKit-Learn.
Looking for the right tech partner to implement the features of Apache Spark to handle your big data processing? Agira would be your right choice, as we are working on providing the best big data solutions using Apache Spark technology. Just say “Hi” to us and let’s discuss this further.
