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Apache Spark 2.0 | Java | Spark | Course

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What is Apache Spark?

Apache Spark is a free and open-source unified analytics engine for processing huge amounts of data. Spark is a programming interface for clusters that has implicit data parallelism and fault tolerance. The Spark codebase was first developed at the University of California, Berkeley's AMPLab and eventually donated to the Apache Software Foundation, which has been maintaining it since. It's a multi-language engine for running data engineering, data science, and machine learning on single-node or clustered servers.

More about Spark

The resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines and maintained in a fault-tolerant manner, is the architectural foundation of Apache Spark. Following the Dataframe API, the Dataset API was published as an abstraction on top of the RDD. The RDD was the primary application programming interface (API) in Spark 1. x, but in Spark 2. x, the Dataset API is encouraged, despite the fact that the RDD API is not deprecated. The Dataset API is still based on RDD technology.

Spark facilitates the implementation of both iterative algorithms, which visit their data set multiple times in a loop, and interactive/exploratory data analysis, i.e., the repeated database-style querying of data. The latency of such applications may be reduced by several orders of magnitude compared to Apache Hadoop MapReduce implementation. Among the class of iterative algorithms are the training algorithms for machine learning systems, which formed the initial impetus for developing Apache Spark.

A cluster manager and a distributed storage system are required for Apache Spark. Spark provides standalone (native Spark clusters for cluster management, where you can manually launch a cluster or utilize the launch scripts provided by the install package. These daemons can also be operated on a single computer for testing purposes), Hadoop YARN, Apache Mesos, or Kubernetes. Spark can connect with a variety of distributed storage systems, including Alluxio, Hadoop Distributed File System (HDFS), MapR File System (MapR-FS), Cassandra, OpenStack Swift, Amazon S3, Kudu, Lustre, or a custom solution. Spark also has a pseudo-distributed local mode, which is often only used for development or testing, and in which distributed storage is not necessary and the local file system can be utilized instead; in this case, Spark is operated on a single machine with one executor per CPU core.

Features of Apache Spark

Data in batches or in real-time

Combine batch and real-time data processing with your chosen programming language: Python, SQL, Scala, Java, or R. Execute rapid, distributed ANSI SQL queries for dashboarding and ad-hoc reporting using SQL analytics. It is faster than the majority of data warehouses.

Data science on a grand scale

Exploratory Data Analysis (EDA) can be performed on petabyte-scale data without the need for downsampling.

Machine learning

Train machine learning algorithms on a laptop, then scale to fault-tolerant clusters of thousands of machines using the same code.

Speed

Spark, which was built from the ground up for performance, can process enormous amounts of data 100 times quicker than Hadoop thanks to in-memory computing and other enhancements. When data is saved on disc, Spark is even faster, and it now holds the world record for large-scale on-disk sorting.

User-Friendliness

Spark features simple APIs for working with huge datasets. This features over 100 data transformation operators as well as standard data frame APIs for managing semi-structured data.

Higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing, are included with a Unified Engine Spark. These standard libraries help developers work more efficiently and can be used to create complicated processes.

What is the purpose of Spark?

Spark can handle several petabytes of data at a time, and it does so by distributing it among thousands of cooperating real or virtual computers. It supports languages like Java, Python, R, and Scala and has a large set of developer libraries and APIs; its versatility makes it well-suited for a variety of use cases. Spark is frequently used with distributed data stores like HPE Ezmeral Data Fabric, Hadoop's HDFS, and Amazon's S3, as well as popular NoSQL databases like HPE Ezmeral Data Fabric, Apache HBase, Apache Cassandra, and MongoDB, and distributed messaging stores like HPE Ezmeral Data Fabric and Apache Kafka.

Some uses of Apache spark

Stream processing-Application developers are increasingly dealing with "streams" of data, ranging from log files to sensor data. This information comes in a steady stream, frequently from numerous sources at the same time. While it is possible to keep these data streams on disc and analyze them later, it is sometimes more practical or vital to evaluate and act on the data received. Financial transaction data streams, for example, can be analyzed in real-time to identify– and reject– possibly fraudulent transactions.

Machine learning-As the amount of data collected increases, machine learning approaches become more viable and accurate. Before applying the same solutions to new and unknown data, the software can be trained to recognize and respond to triggers within well-understood data sets. Spark is a strong choice for training machine learning algorithms because of its ability to store data in memory and conduct repeated queries quickly. Running broadly similar queries at scale dramatically reduces the time it takes to sift through a collection of potential solutions and select the most efficient algorithms.

Rather than running pre-defined queries to create static dashboards of sales, production line productivity, or stock prices, business analysts and data scientists want to explore their data by asking a question, viewing the results, and then either slightly altering the initial question or drilling deeper into the results. This interactive query process necessitates systems that can respond and adapt quickly, such as Spark.

Data integration-Data generated by various systems throughout a company is rarely clean or consistent enough to be merged easily for reporting or analysis. ETL operations are commonly used to extract data from several systems, clean and standardize it, and then load it into a separate system for analysis. Spark (and Hadoop) are becoming more popular for reducing the cost and time necessary for ETL.

How much an Apache Spark developer earns?

To stay and grow in your position, you should be able to learn and adapt to those changes swiftly. An entry-level Spark developer can expect to make anywhere from Rs 6,00,000 to Rs 10,00,000 per year, while an experienced developer can expect to earn anywhere from Rs 25,00,000 to Rs 40,00,000.

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Course Syllabus

Getting Started 11 lectures 17 mins
Introduction Preview 01:42
Welcome guide document 10 Pages
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