Apache Spark™ plays a critical role in the adoption and evolution of Big Data technologies. It is one of the most popular and widely used big data tools. Writing Spark applications is made straightforward by the tool’s ability to work across programming languages. Users can write applications in Scala, Python, Java, and even R.

In addition, Spark Streaming lets users leverage Spark’s language-integrated API. This allows them to create streaming jobs in a similar fashion to writing batch jobs. Spark Streaming possesses impressive fault tolerance, and it can retrieve both operator state and lost work right out of the box without requiring users to write additional code.

Like Spark Applications, users can leverage Spark Streaming with Python, Scala, and Java. These advantages cement Apache Spark’s status as a favorite open-source cluster computing framework.

Despite the occasional use and deployment issues one may encounter with Spark, it still provides more sophisticated ways for enterprises to leverage big data compared to Hadoop. The increasing amount of data being analyzed and processed through the framework is massive and continues to push the boundaries of the engine. 

There are a few challenges and problems with Spark, one of which being library version conflicts between dependencies. How do you overcome this and maximize the value you are getting from Spark? Let Pepperdata Field Engineer Alexander Pierce explain this to you in detail in our Apache Spark tutorial webinar. You can also get an overview of the answer below:

Pierce’s advice is to make sure that any external dependencies and classes you are bringing in do not conflict with internal libraries used by your version of Spark, or those that are available in the environment you are using. For example, many developers may use Google’s Protocol Buffers. Protocol Buffers, (or Protobuf for short),  are Google’s language-neutral, platform-neutral, extensible mechanism for serializing structured data. It is a popular binary format for storing and transporting data, and is just like XML, albeit smaller, faster, and simpler. In this example, let’s say you want to use the getUnmodifiableView() function. That function is only available in Protobuf 2.6.0, while most Hadoop implementations are delivered with Protobuf 2.5.0.  Ultimately, you would need to shade the jar while building your project to avoid conflicts in which Protobuf is being used by your application. 

Watch our Apache Spark tutorial webinar with Pepperdata Field Engineer Alex Pierce to learn more about how to overcome other problems with Spark. This rich learning experience will also help you improve the usability and supportability of your Spark systems.

Or… you can start taking the guesswork out of your Spark systems now. Learn more about the Pepperdata products available here. Better yet, start your free trial now, and see Pepperdata in action!

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