Big data will remain among the in-demand information technologies for a long time to come. According to forecasts, by 2025, businesses will be creating about 60% of the world’s data. Almost continuously, information flows are generated by companies in finance, telecommunications, and e-commerce. Such businesses need technology solutions that can efficiently collect, store, and use large volumes of data. This is one of the reasons why the demand for professionals in big data will only grow in the coming years.
Where does the line between regular and big data cross?
In Russian, the term “big data” is often used in conjunction with the English term “big data. But where is the line that divides the data into “big data” and “normal data”? It is generally accepted that the volume of big data begins with a terabyte, as this amount of data is already difficult to store and process in relational systems. There are other criteria that explain why new methods were needed to handle big data.
Variety. Big data most often consists of unstructured information that can come from multiple sources in a variety of data formats (video and audio files, text, images, etc.). Big data technologies allow processing heterogeneous data simultaneously.
Speed of accumulation. Data is being generated faster and faster. For example, an online store needs to constantly collect information on the number of customers and their purchases. To store such data non-relational databases are better suited, since they can easily be scaled horizontally by adding new servers (most relational databases are scaled vertically by increasing the RAM).
Processing speed. Big data, despite its impressive volume, must be very quickly processed, often – in real time. For example, recommendation systems in online stores instantly analyze customer behavior and produce results on what other products he may like. Such a high speed of processing is achieved by distributed computing.
Thus, for programmers, working with big data implies solving a complex, contradictory problem: how to ensure the collection and storage of constantly growing volumes of heterogeneous data, while achieving very high processing speed.
What you need to know to work with big data
Handling big data is not an easy task for programmers, and new methods and tools are constantly emerging to solve it. However, there is a basic stack of technologies that are most often found in job postings.
- Apache Hadoop is a platform for parallel processing and distributed data storage. It represents a large set of tools, the most important of which are Hadoop Distributed File System, or HDFS (data storage), and MapReduce (data processing).
- Apache Spark is a framework for parallel data processing. It can be used to process streaming data in real time. It supports in-memory processing to improve performance for big data applications.
- Apache Kafka – a platform for streaming data processing. The advantages of the technology are high speed of data processing and its safety (all messages are replicated).
- Apache Cassandra is a distributed non-relational database management system. It consists of multiple storage nodes and is easily scalable. Cassandra is fault tolerant – all data is automatically replicated between cluster nodes.
What programming paradigms are used
When working with big data, you will refer to different programming paradigms: imperative, declarative, and parallel. For example, SQL programming for databases uses the declarative approach, where you specify the task and the desired result without specifying intermediate steps. To cope with the fast processing of large data sets, you need to apply multi-threaded and parallel computing. One solution is the MapReduce paradigm. It is a computational model for parallel processing of distributed data. The algorithm allows to divide tasks between cluster machines, so that data processing takes place simultaneously on all involved computers.
What you can do in big data
Work with big data can be divided into two areas. The first is creating and maintaining the infrastructure for collecting, storing, and processing data, and the second is analyzing and extracting useful information. Each of these processes requires specialists with knowledge of programming. The first direction involves familiar IT specialties – engineers, architects, administrators, but possessing technologies for working with big data. Data analysis is a narrower specialization, often requiring a specific education or experience.
Big data engineer
Develops technical solutions related to storing and processing big data. He may also be responsible for the delivery and distribution of data from different sources (website, social networks, sensors, etc.) to the storage system.
Database architect (Data architect).
Designs databases based on the needs of the organization that collects the information. The architect chooses the technology to store and process the data.
Database manager
Monitors the performance of the databases and troubleshoots database performance as needed. The specialist ensures that the servers are up and running at all times so that the data always remains intact.
Big data analyst.
Conducts descriptive data analysis, makes data visualization. An analyst finds meaningful information in scattered data sets.
Data scientist.
Looks for hidden patterns, trends in datasets, based on the specific task. Excellent knowledge of mathematics and statistics is mandatory for this specialty – you need to apply machine learning algorithms and build predictive models.
The list of career opportunities in big data is constantly expanding – as technology advances, new, narrower areas appear:
Data governance manager
Responsible for the data collection process. He decides what data to collect and how to store it. He is also in charge of data verification.
Data security administrator
Creates a strategy to protect the data from unauthorized access. The data security administrator can monitor the security of databases and implement security features.
