Overview of Spark
Spark is an open source unified analytics engine and set of libraries designed to perform various forms of big data processing tasks, and has become widely utilized due to its speed, flexibility and scalability.
Executes applications across a cluster of physical or virtual machines and supports several programming languages such as Java, Python, Scala, R and Julia. Furthermore, it integrates seamlessly with multiple data storage systems like HDFS and Amazon S3 and provides developers with access to an impressive set of developer libraries.
Spark has become the go-to solution for big data analytics due to its scalability, usability, and ease of use - and has become widely adopted by major corporations like TripAdvisor for natural language processing of reviews as well as by Under Armour's fitness application MyFitnessPal for internal marketing demographic classification.
Architecture of Spark
Spark utilizes cluster computing, linking together many computer processors for faster analysis. This makes Spark an scalable solution; more processing power can be added when necessary. Furthermore, distributed storage enables large data sets to be quickly read/written from.
As soon as user application code is submitted to a driver program, it converts it to a DAG with multiple stages and physical execution units called tasks. Executors run these tasks concurrently before returning their results back to the driver program - any transformations performed will only compute results once an action has taken place on them.
RDDs in Spark
Spark's RDDs (Resilient Distributed Datasets) are central to its parallel processing. Immutable, memory efficient and fault tolerant, RDDs provide an ideal way to cache data across processes while sharing across processes.
RDDs are logically distributed among multiple servers to make parallel computing easier across a cluster. Furthermore, RDDs include lineages that allow them to rebuild themselves in case of any failures or crashes.
RDDs can be saved using Spark's persist() method to store them into stable storage or memory, making them available for later tasks that use its cache() method. Spark also utilizes named accumulators to track task progress; you can view these status indicators via its web UI under "Accumulator" tab.
Streaming in Spark
Real-time information is highly beneficial in business settings. According to IBM, 60 percent of sensory data loses value within milliseconds if left unused; mastering Spark streaming allows businesses to quickly take advantage of this data source.
Spark Streaming stands out from Kafka with its superior fault tolerance and seamless integration of advanced processing libraries such as SQL, machine learning and graph processing. You can join streams against historical data or run ad-hoc queries against stream state. Distributed computation and parallelization help increase throughput while keeping latency close to milliseconds; its streaming API utilizes Spark RDDs which reexecute tasks when failures occur; in fact a DStream is simply a sequence of these RDDs for higher level abstraction.
Machine Learning in Spark
Apache Spark, an open-source cluster-computing platform, provides an ideal ecosystem for machine learning and predictive analytics, offering superior scalability and simplicity than tools such as Hadoop.
Learn Python with Spark MLlib to create end-to-end machine learning workflows on Spark. Combine and apply multiple algorithms using Spark MLlib in order to address real world data issues.
MLlib employs Spark SQL's SchemaRDD to support a wide variety of data types under one Dataset concept. For instance, feature transformers could read in an existing dataset, transform its columns (e.g. feature vectors) into new columns in an updated dataset, then output their updated dataset. Model transformers might apply a learning algorithm on it to produce an Estimator Estimator as part of their output process.
