Apache Hadoop has been the foundation for big data applications for a long time now, and is considered the basic data platform for all big-data-related offerings. However, in-memory database and computation is gaining popularity because of faster performance and quick results. Apache Spark is a new framework which utilizes in-memory capabilities to deliver fast processing (almost 100 times faster than Hadoop). So, the Spark product is increasingly being used in a world of big data, and mainly for faster processing.
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What Is Apache Spark?
Apache Spark is an open-source framework for processing huge volumes of data (big data) with speed and simplicity. It is suitable for analytics applications based on big data. Spark can be used with a Hadoop environment, standalone or in the cloud. It was developed at the University of California and then later offered to the Apache Software Foundation. Thus, it belongs to the open-source community and can be very cost-effective, which further allows amateur developers to work with ease. (To learn more about Hadoop's open source, see What Is the Influence of Open Source on the Apache Hadoop Ecosystem?)
The main purpose of Spark is that it offers developers with an application framework that works around a centered data structure. Spark is also extremely powerful and has the innate ability to quickly process massive amounts of data in a short span of time, thus offering extremely good performance. This makes it a lot faster than what is said to be its closest competitor, Hadoop.
Why Spark Is so Important Over Hadoop
Apache Spark has always been known to trump Hadoop in several features, which probably explains why it remains so important. One of the prime reasons for this would be to consider its processing speed. In fact, as stated above already, Spark offers about 100 times faster processing than Hadoop’s MapReduce for the same amount of data. It also uses significantly fewer resources as compared to Hadoop, thereby making it cost-effective.
Another key aspect where Spark has the upper hand is in terms of compatibility with a resource manager. Apache Spark is known to run with Hadoop, just as MapReduce does, however, the latter is currently only compatible with Hadoop. As for Apache Spark, however, it can work with other resource managers such as YARN or Mesos. Data scientists often cite this as one of the biggest areas where Spark really outdoes Hadoop.
When it comes to ease of use, Spark again happens to be a lot better than Hadoop. Spark has APIs for several languages such as Scala, Java and Python, besides having the likes of Spark SQL. It is relatively simple to write user-defined functions. It also happens to boast an interactive mode for running commands. Hadoop, on the other hand, is written in Java and has earned the reputation of being pretty difficult to program, although it does have tools that assist in the process. (To learn more about Spark, see How Apache Spark Helps Rapid Application Development.)
What Are Spark's Unique Features?
Apache Spark has some unique features that truly distinguish it from many of its competitors in the business of data processing. Some of these have been outlined briefly below.
One of the unique aspects of Apache Spark is its unique "in-memory" technology that allows it to be an extremely good data processing system. In this technology, Spark loads all of the data to the internal memory of the system and then unloads it on the disk later. This way, a user can save a part of the processed data on the internal memory and leave the remaining on the disk.
Spark also has an innate ability to load necessary information to its core with the help of its machine learning algorithms. This allows it to be extremely fast.
Spark’s core manages several important functions like setting tasks and interactions as well as producing input/output operations. It can be said to be an RDD, or resilient distributed dataset. Basically, this happens to be a mix of data that is spread across several machines connected via a network. The transformation of this data is created by a four-step method, comprised of mapping the data, sorting it, reducing it and then finally, joining the data.
Following this step is the release of the RDD, which is done with support from an API. This API is a union of three languages: Scala, Java and Python.
Apache Spark’s SQL has a relatively new data management solution called SchemaRDD. This allows the arrangement of data into many levels and can also query data via a specific language.
Apache Spark comes with the ability to process graphs or even information that is graphical in nature, thus enabling the easy analysis with a lot of precision.
This is a prime part of Spark that allows it to stream large chunks of data with help from the core. It does so by breaking the large data into smaller packets and then transforming them, thereby accelerating the creation of the RDD.
MLib – Machine Learning Library
Apache Spark has the MLib, which is a framework meant for structured machine learning. It is also predominantly faster in implementation than Hadoop. MLib is also capable of solving several problems, such as statistical reading, data sampling and premise testing, to name a few.
Why Spark Is Not a Replacement for Hadoop
Despite the fact that Spark has several aspects where it trumps Hadoop hands down, there are still several reasons why it cannot really replace Hadoop just yet.
First off, Hadoop simply offers a larger set of tools when compared to Spark. It also has several practices that are recognized in the industry. Apache Spark though, is still relatively young in the domain and will need some time to get itself up to par with Hadoop.
Hadoop’s MapReduce has also set certain industry standards when it comes to running full-fledged operations. On the other hand, it is still believed that Spark isn’t entirely ready to operate with complete reliability. Often, organizations that use Spark need to fine tune it, in order to make it ready for their set of requirements.
Hadoop’s MapReduce, having been around for a longer time than Spark, is also easier to configure. This isn’t the case for Spark though, considering that it offers a whole new platform that hasn’t really tested rough patches.
What Companies Think About Spark and Hadoop
Many companies have already started to make use of Spark for their data processing needs, but the story doesn’t end there. It surely has several strong aspects that make it an amazing data processing platform. However, it also comes with its fair share of drawbacks that need fixing.
It is an industry notion that Apache Spark is here to stay and is even possibly the future for data processing needs. However, it still needs to undergo a lot of development work and polishing that will allow it to truly harness its potential.
Apache Spark has been and is still being employed by numerous companies that suit their data processing requirements. One of the most successful implementations was carried out by Shopify, which was looking to select eligible stores for business collaborations. However, its data warehouse kept timing out when it wanted to understand the products its customers were selling. With the help of Spark, the company was able to process several million data records and then process 67 million records in a few minutes. It also determined which stores were eligible.
Making use of Spark, Pinterest is able to identify developing trends and then uses it to understand the behavior of users. This further allows for better value in the Pinterest community. Spark is also being used by TripAdvisor, one of the world’s largest travel information sites, to speed up its recommendations to visitors.
One cannot doubt Apache Spark’s prowess, even at present, and the unique set of features that it brings to the table. Its processing power and speed, along with its compatibility sets the tone for several things to come in the future. However, it also has several areas it needs to improve on, if it is to truly realize its full potential. While Hadoop still the rules the roost at present, Apache Spark does have a bright future ahead and is considered by many to be the future platform for data processing requirements.