The New Age of Data Explosion!
Data explosion marks the current era! So, the next time you run a Google search, think about the fact that this search is just one out of the two million searches Google gets that very minute. Online activities create data constantly. This goes unnoticed by many of us. Most firms hence struggle to seek an answer to the following question- How can one find out what their customers want and offer the same to them? Firms also want to offer instant solutions to their customers’ needs. Thus, the need to process large-scale data quickly becomes vital. Enter Apache Spark. This lightning fast cluster computing framework was designed to cater to this very need.
Originally developed at UC Berkeley in 2009, Apache Spark is the largest open source project in data processing. As said earlier, the volume of network traffic has been increasing. This further increases the need for network monitoring systems that capture network packets and provide packet features in near real-time. As a first step towards making such a system using distributed computation, a new application has been developed using Spark. This application extracts packet features at a fast rate consuming less memory. Its streaming capability analyses traffic. By analyzing the network data features it also provides a means for detecting attacks.
Why would I want to use Spark?
Apache Spark and MapReduce are similar in nature. Both provide parallel distributed processing, fault tolerance on hardware, scalability, etc. Yet, Apache Spark brings with it a host of other benefits which outperforms MapReduce on multiple aspects-
1. 100x faster
The high speed can be attributed to its in-memory computations. MapReduce needs a lot of time to perform input/output operations that increase latency. Also, by running in-memory, Spark eliminates the time spent on moving data/processes in/out of disks. Thus, the time taken by Apache Spark to process and respond to applications is much faster than the time taken by disks.
Provides easy-to-use APIs (i.e. Application Program Interface) for operating on large datasets. Spark enables the user to write applications quickly in Java, Scala, R, and Python. It also reduces difficulty by doing away with the need of having any abstractions.
3. Greater generality
There are additional libraries which can be used for SQL, Machine Learning, Streaming, and Graph Processing. Such libraries can be seamlessly combined into one application.
It’s a Big Data analytics tool offering real-time Big Data analysis. Apache Spark instantly processes data generated by real-time event streams (which is coming in @ of millions!). (For e.g., the millions of tweets that Twitter gets per second.) The Spark Streaming also allows modification of real-time data.
In short, it provides cached in-memory distributed computing, low latency, high-level APIs, etc. This saves time as well as money. Besides having a high processing power, it is compatible with the most popular technologies of a data ecosystem. For these reasons, Spark proves to be more beneficial than other tools.
Who uses Spark?
Know more about Spark’s Unified Libraries
1. Spark SQL
It is a Spark module which allows working with structured data. It supports data querying via SQL or Hive Query Language.
Use cases: Traditional ETL (Extract, Transform, and Load), analytics, and reporting.
2. Spark Streaming
This makes it easy to build scalable applications which provide fault-tolerant streaming. It also processes web server logs, tweets, Facebook posts, etc. in real-time.
Use cases: It recovers both lost work and operator state (e.g. sliding windows). Thus, eliminating the need to write extra codes. It drastically increases performance whilst streaming videos and music. This spares the user to wait indefinitely while streaming videos or music.
3. MLlib (Machine Learning)
This is a scalable machine learning library. It provides various algorithms for classification, regression, clustering, etc. Apache Mahout (i.e. ML library for Hadoop) has shifted from Hadoop MapReduce to Apache Spark.
Use cases: Its high-quality algorithms (which are 100x faster than Map Reduce) finds uses in anomaly detection, social analytics, providing recommendations, etc.
GraphX is an API for graphs. It provides a uniform tool for ETL, exploratory analysis, and iterative graph computation.
Use cases: It provides a library of common graph algorithms such as PageRank. GraphX module can efficiently find the shortest path for static graphs.
Companies using Spark
Since Spark’s release, there has been a rapid rise in the number of firms (across a wide range of industries) that are adopting the tool. Internet powerhouses such as Netflix, Yahoo, Baidu, Airbnb, eBay and Tencent, have deployed Spark at a massive scale.
PERSONALIZATION @ YAHOO!:
Yahoo! highly personalizes properties. It has highly sophisticated algorithms. This helps it to respond rapidly to users’ activities and events that take place real-time. This maximizes relevance for its users too. Spark at Yahoo! runs in Hadoop YARN to use existing data and clusters.
Yahoo developers have been successful with some Spark projects. One such example is the stream ads project. Here, coders wrote only 120 lines of code in Scala as compared to writing 15000 lines in C++ for the same project.
CONVIVA REAL-TIME VIDEO OPTIMIZATION:
Conviva is one of the largest video companies on the internet. It manages over 4 billion video streams per month. It enables viewers to move on to another video in case they don’t receive the high-quality experience they expect. This is done through dynamic selection of an optimized source, while a video is playing, to maximize the quality. Thanks to Conviva, data showing tolerance for poor-quality videos has been declining quickly.
Using Spark Streaming, Conviva learns network conditions in real-time. It also feeds results directly to video players to optimize streams.
OTHER COMPANIES USING SPARK:
Technology giants such as Netflix, Uber, and Pinterest use Spark too!
To sum it up
Spark has found many use cases in the Big Data technology ecosystem despite being a relatively new technology. Moreover, it has successfully integrated the essential technologies into the Big Data Universe. Also, Spark quickly processing large-scale data. It is also developer-friendly in nature whilst faulting tolerance for errors. These merits make it easy-to-use while being time and cost-effective.
Indeed, the uses of Spark listed above is just the tip of the iceberg. The uses of Spark are to increase by leaps and bounds. Today, there is a pressing need to manage and analyze Big data. Hence, more and more firms are likely to leverage Spark for the simplicity and real-time analysis it provides.