There’s no single technology that encompasses big data analytics. Of course, there’s advanced analytics that can be applied to big data, but in reality several types of technology work together to help you get the most value from your information.
Here are the biggest players:
Cloud computing, Data management, Data storage, including the data lake and data warehouse, Hadoop, In-memory analytics, Machine learning, Predictive analytics, Text mining
Cloud computing. A subscription-based delivery model, cloud computing provides the scalability, fast delivery and IT efficiencies required for effective big data analytics. Because it removes many physical and financial barriers to aligning IT needs with evolving business goals, it is appealing to organizations of all sizes.
Data management. Data needs to be high quality and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an organization, it’s important to establish repeatable processes to build and maintain standards for data quality. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.
Data mining. Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions. With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what’s relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.
Data storage, including the data lake and data warehouse. It’s vital to be able to store vast amounts of structured and unstructured data – so business users and data scientists can access and use the data as needed. A data lake rapidly ingests large amounts of raw data in its native format. It’s ideal for storing unstructured big data like social media content, images, voice and streaming data. A data warehouse stores large amounts of structured data in a central database. The two storage methods are complementary; many organizations use both.
Hadoop. This open-source software framework facilitates storing large amounts of data and allows running parallel applications on commodity hardware clusters. It has become a key technology for doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop’s open-source framework is free and uses commodity hardware to store and process large quantities of data.
In-memory analytics. By analyzing data from system memory (instead of from your hard disk drive), you can derive immediate insights from your data and act on them quickly. This technology is able to remove data prep and analytical processing latencies to test new scenarios and create models; it’s not only an easy way for organizations to stay agile and make better business decisions, it also enables them to run iterative and interactive analytics scenarios.
Machine learning. Machine learning, a specific subset of AI that trains a machine how to learn, makes it possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
Predictive analytics. Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It’s all about providing the best assessment of what will happen in the future, so organizations can feel more confident that they’re making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, risk, operations and marketing.
Text mining. With text mining technology, you can analyze text data from the web, comment fields, books and other text-based sources to uncover insights you hadn’t noticed before. Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.
Big Data technologies help organizations collect, store, process, and analyze massive datasets to derive valuable insights. Here are some essential technologies that drive Big Data processes:
Hadoop Ecosystem
Apache Hadoop is a foundational technology for Big Data, built to handle large-scale data processing using distributed computing. It consists of:
- HDFS (Hadoop Distributed File System): HDFS breaks down data into blocks and distributes them across a cluster of servers. This allows for parallel data processing, ensuring high availability and fault tolerance.
- MapReduce: A programming model that processes data in two main steps: ‘Map’ breaks down tasks into smaller, manageable sub-tasks, and ‘Reduce’ aggregates the results to produce a final output. This approach handles large data volumes efficiently.
- YARN (Yet Another Resource Negotiator): Manages and allocates resources within the Hadoop cluster. It allows for better scalability and resource utilization.
- Hive and Pig: Hive is a data warehouse system for managing and querying structured data using an SQL-like language, while Pig is a high-level platform for analyzing large datasets with a more procedural scripting language.
Apache Spark
Apache Spark is a lightning-fast Big Data processing engine. Unlike Hadoop’s MapReduce, Spark processes data in memory, which speeds up tasks significantly. It’s ideal for machine learning, stream processing, and interactive queries. Spark includes:
- Spark Core: The primary engine that handles tasks like scheduling and monitoring.
- Spark SQL: A module for working with structured data using SQL queries.
- Spark Streaming: Processes real-time data streams.
- MLlib: A scalable machine learning library.
- GraphX: A framework for graph computation and analysis.
NoSQL Databases
Traditional relational databases struggle with unstructured and semi-structured data. NoSQL databases address this by offering high flexibility, speed, and scalability. Key types include:
- MongoDB: A document-based database that stores data in JSON-like format, suitable for applications needing flexible schema.
- Cassandra: A distributed database designed for handling large amounts of data across multiple servers with high fault tolerance.
- HBase: A Hadoop-based database that provides real-time read and write access to large datasets. It is used for random, real-time access to Big Data.
Data Processing and Stream Analytics
For real-time data analysis, technologies like Apache Kafka and Apache Flink are essential.
- Apache Kafka: A distributed event streaming platform that handles high-throughput data feeds. It’s used for building real-time streaming data pipelines and applications that react to data in real-time.
- Apache Flink: A stream-processing framework that handles both batch and real-time analytics. It is highly efficient in managing time-based event processing.
Data Warehousing and Analytics
Technologies like Amazon Redshift, Google BigQuery, and Snowflake provide cloud-based data warehousing solutions that allow organizations to store and analyze data at scale. These platforms leverage distributed computing to perform complex queries quickly.
Machine Learning and Data Mining
Frameworks like TensorFlow, PyTorch, and Apache Mahout facilitate building and deploying machine learning models on large datasets. These technologies enable tasks like predictive analytics, classification, and clustering.
Data Visualization Tools
Tools like Tableau, Power BI, and Apache Superset help interpret Big Data insights. They convert raw data into interactive dashboards and reports, making information accessible to non-technical stakeholders.
