Data processing technologies are developing as rapidly as data collection is advancing, that is, at a continually accelerating rate. There’s a whole lot of technology that is breaking ground and offering new solutions in this exciting field.
Let’s take a look at what some of the latest cutting-edge technologies are for data processing systems.
Distributed Systems Architecture
Big data sets common in data processing today have limitations on computational power. The technology needed to deal with this is called distributed systems architecture.
MPP – Massive parallel processing, and Hadoop are two key technologies that are leading the industry in distributed systems architecture. Both feature the “shared nothing” technology that ensures autonomous operation.
The key difference between the two is that MPP is proprietary and rather costly to implement, while Hadoop is open source and can be integrated from very small, low cost applications, to very very large ones. While Hadoop is more recent than MPP, and allows flexibility and scalability, MPP remains slightly quicker.
MPP systems are provided by Teradata, Netezza, Vertica, and Greenplum. Oracle and Microsoft also have their own MPP systems.
Hadoop is a software project by Apache, containing a collection of software utilities that provide huge storage and processing power. Hadoop uses MapReduce to process large non-structured data sets, as the name implies, by a map function, and a reduce function within Hadoop. Many platforms can be built on top of the Hadoop framework. Non-proprietary applications available for use on Hadoop continue to develop in number and complexity.
Part of leading technology for data processing in a relational database is query optimization design. Query optimization is an automated process that attempts to provide the best possible answers based on a range of possible query plans. A query plan is a set of rules that a relational database uses to search data for the required parameters. Query optimization can effectively determine which searches are valid, and which will be most accurate, efficient, and timely.
Query hints may be built into query optimization, for example, a query on a GPS database might be selected for the fastest or the quickest route. A simplified example of query optimization is to imagine a query for the number of a certain car make and model, where the database could search all makes then all models, just all models, since the model subset automatically includes make. Query optimization would choose the latter.
Non-Relational databases – No-SQL
With the explosion of Big-Data, has come two more players in data processing technology, non structured and dark data.
Traditional databases have relational structure, usually called relational data base management systems (RDBMS), and are primarily built on SQL – structured query language, which is why non-relationship databases are coined No-SQL.
A Non-relational, No-SQL database can store and access un-structured data easily using a common data format called JSON documents, and can import JSON, CSV, and TSV formats.
Popular No-SQL databases used in data processing are MongoDB, Arango DB, Apache Ignite, and Cassandra.
Data storage and retrieval can sometimes deteriorate data due to the format that is required by the storage or retrieval. Unlike the traditional ETL (extract, transform, load) data method, in data virtualization the data remains where it is, a viewer accesses it in real time, from it’s existing location, solving the problem of format losses. An abstraction layer between viewer and source means that the data can be used without extraction and transformation.
A simplified example of data virtualization we can all identify with is the technology that drives images on social media. When you view an image on most social media platforms, normally you’re viewing it temporarily in real time on your mobile device or computer, but it exists in reality on the server of whichever social media you’re on. The file format is not relevant, nor do you need software related to the format to view it. The image is only converted into real data if it’s downloaded or via a screenshot, but the data is searchable and viewable without ever opening the file itself because of data virtualization.
Stream Processing and Stream Analytics
Stream processing provides the capability for performing actions and analyzing events on real-time data. To do this stream processing makes use of a series of continuous queries. Stream processing allows data information to be processed before it lands in a database, which makes it incredibly powerful.
A good example to explain the process of live stream data analytics is the correlation of GPS data or driver mobile data with user locations. Uber’s apps have used this with great success to revolutionize private transport. Many bank applications also use stream processing to immediately alert users of suspicious activity.
Striim, IBM Infosphere, SQLStream, and Apache Spark are examples of common streaming database applications.
Data Mining and Scraping
Data mining and scraping technology is improving the content that data-processing systems have available in the data capture phase. Data mining in it’s simplest form essentially takes very large sets of data and extracts smaller more useful sets. Data mining software automizes the fundamental data processing function of finding patterns in large data sets, to create smaller subsets which match search query criteria. Web search is essentially a form of data mining we all use, taking the catalogue of websites and extracting only those that match search terms. Data mining may be applied to any type of data, text, audio, video, images. Data mining can be incredibly useful in finding information a company doesn’t currently have from large unstructured data sources.
Scraping is similar to mining, but where mining analyzes data for patterns, scraping collects data matching certain parameters.
Machine Learning and AI
Data processing is a key field for advances in machine learning and AI. Data preparation involves cleaning and transforming the data for us. It often takes around 60 to 80% of the whole data processing time, with as little as 20% for analytics and presentation. The preparation of data is largely repetitive and time consuming, so it is a perfect area for implementation of the latest technology in machine learning. Processing large amounts of data, especially when complex text based data like searching contracts, reports, articles, machine learning is a one of the latest technological advancements that will improve the industry. Machine learning can match phrases in a range of documents based on connections that previously only humans could do. We think of AI and machine learning as way out there, but we actually interact with it every day on platforms like Google search. Haven’t you noticed how it seems to know more and more what you might be thinking, with scary accuracy? It’s a simple concept yet, currently one of the most extensive examples of machine learning data processing in everyday use. Machine learning is also growing steadily in user interaction devices on the web. Automated answers to users questions, along with databasing questions and responses for improved machine learning, helps organizations better serve their customers.
AI and machine intelligence is advancing faster than we can train people to work with it. An unbelievable 2 jobs are available for every AI graduate in the UK.
Compression is driving data processing, with larger and larger data sets, any reduction in data sizes will improve experiences. Storage space and processing times can be reduced significantly with better compressions techniques, this in turn significantly reduces costs and improves performance. Facebook has released their latest compression tool Z standard on an open source platform. While previous storage compression devices had around 9 levels, Z standard has 22 levels. Data compression will help improve our storage and processing capacities.
Self-Driving Database Management Systems
The last and most significant technology in data processing systems is the self-driving database management system. A self-driving database can be run without user intervention, and totally managed by the user. Leading this technological advancement is Oracle’s Autonomous Database. Oracle’s founder claims it will revolutionize data management, since there is no need to apply patches, complete manual back-ups, or tune, it’s capable of total automation. Peleton is a good example of a leading open source autonomous database solution.
For data processing, it’s important to stay ahead of the trends. Check out some of the ideas we’ve discussed here to find out more about where your data processing systems can evolve.