You can process this data using Apache Spark's GraphX API. You might use a graph database if you have hierarchial data or data with interconnected relationships. Graph processing through GraphXĪ graph is a collection of nodes connected by edges. Apache Spark's machine learning library, MLlib, contains several machine learning algorithms and utilities. Your computer can use existing data to forecast or predict future behaviors, outcomes, and trends. ![]() Machine learning is used for advanced analytical problems. You can filter, aggregate, and prepare very large datasets using long-running jobs in parallel. Batch processingīatch processing is the processing of big data at rest. Apache Spark supports real-time data stream processing through Spark Streaming. Just like relational data, you can filter, aggregate, and prepare streaming data before moving the data to an output sink. Real-time data can be processed to provide useful information, such as geospatial analysis, remote monitoring, and anomaly detection. Telemetry from IoT devices, weblogs, and clickstreams are all examples of streaming data. Streaming, or real-time, data is data in motion. There are several ways to transform data, including: Extract, transform, and load (ETL)Įxtract, transform, and load (ETL) is the process of collecting data from one or multiple sources, modifying the data, and moving the data to a new data store. Spark is a general-purpose distributed processing engine that can be used for several big data scenarios. ![]() You might consider a big data architecture if you need to store and process large volumes of data, transform unstructured data, or process streaming data. Spark processes large amounts of data in memory, which is much faster than disk-based alternatives. Big data solutions are designed to handle data that is too large or complex for traditional databases. Apache Spark is an open-source parallel processing framework that supports in-memory processing to boost the performance of applications that analyze big data.
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