#INSTALL JUPYTER NOTEBOOK ON WINDOWS INSTALL#

Install findspark, to access spark instance from jupyter notebook.

#INSTALL JUPYTER NOTEBOOK ON WINDOWS LICENSE#

Install JavaД«efore you can start with spark and hadoop, you need to make sure you have installed java (vesion should be at least java8 or above java8).Go to JavaвЂ™s officialВ download website, accept Oracle license and download Java JDK 8, suitable to your system. Integrating Python with Spark is a boon to them.

Majority of data scientists and analytics experts today use Python because of its rich library set. PySpark offers PySpark ShellВ which links the Python API to the spark core and initializes the Spark context. It is because of a library called Py4jВ that they are able to achieve this. Using PySpark, you can work with RDDsВ in Python programming language also. To support Python with Spark, Apache Spark Community released a tool, PySpark. PySpark вЂ“ OverviewДЄpache Spark is written in Scala programming language.

It also provides an optimized runtime for this abstraction. It provides an API for expressing graph computation that can model the user-defined graphs by using Pregel abstraction API.

GraphX is a distributed graph-processing framework on top of Spark. Spark MLlib is nine times as fast as the Hadoop disk-based version of Apache MahoutВ (before Mahout gained a Spark interface). It is, according to benchmarks, done by the MLlib developers against the Alternating Least Squares (ALS) implementations. MLlib is a distributed machine learning framework above Spark because of the distributed memory-based Spark architecture. It ingests data in mini-batches and performs RDD (Resilient Distributed Datasets) transformations on those mini-batches of data. Spark Streaming leverages Spark Core's fast scheduling capability to perform streaming analytics. Spark SQL is a component on top of Spark Core that introduces a new data abstraction called SchemaRDD, which provides support for structured and semi-structured data. It provides In-Memory computing and referencing datasets in external storage systems. Spark Core is the underlying general execution engine for spark platform that all other functionality is built upon. The following illustration depicts the different components of Spark. It also supports SQL queries, Streaming data, Machine learning (ML), and Graph algorithms. Advanced AnalyticsВ в€’ Spark not only supports вЂMapвЂ™ and вЂreduceвЂ™.Spark comes up with 80 high-level operators for interactive querying.

Therefore, you can write applications in different languages.

Supports multiple languagesВ в€’ Spark provides built-in APIs in Java, Scala, or Python.

It stores the intermediate processing data in memory. This is possible by reducing number of read/write operations to disk.

SpeedВ в€’ Spark helps to run an application in Hadoop cluster, up to 100 times faster in memory, and 10 times faster when running on disk.

Apart from supporting all these workload in a respective system, it reduces the management burden of maintaining separate tools. Spark is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries and streaming. The main feature of Spark is its in-memory cluster computingВ that increases the processing speed of an application. It is based on Hadoop MapReduce and it extends the MapReduce model to efficiently use it for more types of computations, which includes interactive queries and stream processing. Apache Spark is a lightning-fast cluster computing technology, designed for fast computation.

Install jupyter notebook on windows