As the market for big data analytics rapidly expands to include mainstream customers, its important to know big data technologies that really matter.
Everyone’s talking about data science, with its predictive modeling, data mining, and machine learning. But most of this would not be possible, especially on a large scale, without data engineering. Listed below are few big data technologies that every data engineer must know.
1.Predictive analytics: This technology, which includes both hardware and software solutions, will help your firm discover, evaluate, optimize, and deploy predictive models. This it does by analyzing big data sources, thereby improving business performance or mitigating risk.
2.NoSQL database: In comparison to their RDBMS counterparts, NoSQL databases are enjoying exponential growth. The NoSQL database type offers dynamic schema design, offering the potential for increased customization, flexibility, and scalability, that’s much needed when storing big data.
3.Search and Knowledge discovery: You need to know these tools and technologies for the self-service extraction of information. Search and knowledge discovery is about gaining new insights from large repositories of both structured, as well as unstructured data that resides in sources, such as file systems, streams, databases, APIs, and other platforms and applications.
4.Stream analytics: If you need to aggregate, filter, enrich, and analyze a high throughput of data. Stream analytics looks into data that comes from multiple, disparate, and live data sources and in varying formats.
5.In-memory data fabric: This technology provides low-latency access and lets you process large quantities of data. It distributes data across dynamic random access memory (DRAM), SSD, or Flash of a distributed computer system.
6.Distributed file stores: A computer network that stores data on more than one node, often in a replicated fashion, to deliver redundancy and performance.
7.Data virtualization: If you need information that’s delivered from various big data sources, such as Hadoop and distributed data stores, in real-time and near real-time, data virtualization is your technology.
8.Data integration: Data integration is about tools that enable data orchestration across solutions such as Apache Hive, Apache Pig, Amazon Elastic Map Reduce (EMR), Hadoop, Couchebase, MongoDB, Apache Spark, etc.
9.Data preparation: To ease the burden of shaping, cleansing, sourcing, and sharing messy and diverse data sets that accelerate data’s usefulness for analytics.
10.Data quality: The technology that conducts data cleansing and enrichment on high-velocity, large data sets. It utilizes parallel operations on distributed databases and data stores.
Big data technologies: things to note
All of these tools contribute to real-time, predictive, and integrated insights; exactly what big data customers want now. To gain the competitive edge that big data offers, you need to infuse analytics everywhere, exploit value in all types of data, and make a speed differentiator. All of this requires an infrastructure that can manage and process massive volumes of structured and unstructured data. Big data technologies must support search, governance, development, and analytics services for data that ranges from transaction and application data to machine and sensor data, to geospatial, social, and image data.