Big Data Development Company

The notion covers bulky data sets too intricate to process manually or with conventional software. Big data is characterized by great variety, large volume, and high velocity, a.k.a. three Vs, and can be structured, semi-structured, and unstructured. Also, it lays the groundwork for predictive analytics, machine learning, artificial intelligence, business intelligence, and data warehouses development and management.

As a rule, big data is kept in data lakes — special repositories capable of storing large amounts of information in a raw format. Data lakes are a cost-efficient solution that simplifies further big data management and analysis.

When a service provider offers advanced cloud-based platforms and tools that facilitate data analysis, management, and integration, we are witnessing Big Data as a Service (BDaaS).

DevOps (development and operations) is an approach to software development aimed to reorganize the workflow by eliminating cross-departmental silos, employing continuous delivery, and automating most operational processes. Key DevOps components are CI/CD framework, configuration management, product stability, and Infrastructure-as-a-Code.

A big data DevOps engineer elaborates efficient strategies for developing and maintaining big data processing systems owned by an enterprise.

Such tech-savvy specialists are responsible for the full cycle of big data development, i.e., for data processing system design, creation, testing, and maintenance.

Both a data developer and a data engineer are trained in the craft of the creation and support of big data solutions. However, while big data developers cooperate with data analysts and database administrators and are more focused on data pipelines elaboration, data engineering presupposes ETL/ELT process development and work with data architects and data scientists.

By leveraging big data technologies, these experts scrutinize the information stored in organizational systems to extract hidden patterns, reveal unobvious correlations, unveil market trends, and create detailed reports. Armed with such actionable insights, businesses can enhance operational efficiency, predict customer behavior, and optimize decision-making.

Both abbreviations are related to data integration pipelines.

The term “ETL” describes the process of data delivery to a single repository in a structured format: first, the information is extracted from a data source, then, it's transformed into the required format, and after that, it is loaded to a data warehouse.

The “ELT” process has a similar goal which is, however, reached with a different sequence of steps. First, the extracted raw data is loaded to the chosen system and only then it gets transformed, if necessary.

Data can be divided into the following groups:

1. Structured data (information adjusted to a machine-readable format);
2. Semi-structured and unstructured data (requires human interpretation partly or entirely);
3. Big data (data sets too huge to be processed by traditional technology);
4. Raw data (primary-source information, either in a structured or unstructured format, that needs cleaning and processing);
5. Metadata (information describing data properties);
6. Master data (information related to a particular business)

Besides, there can be real-time (used at the moment) and historical (collected and stored) data.

When a business analysis team led by a data scientist scrutinizes giant sets of raw data to retrieve valuable insights, we observe the process of data mining. Such techniques as clustering, decision tree creation, classification, regression analysis, and others are employed for this purpose.

Business Intelligence (BI) is the practice of organizational data analysis by means of special technologies and strategies. BI has sophisticated analytics tools, data mining, and big data visualization at its core and helps enterprises predict future outcomes, enhance customer service, get a clear vision of their business processes, and much more.

The key BI categories are:

• Descriptive analytics (interprets historical data);
• Prescriptive analytics (analyzes historical data);
• Predictive analytics (anticipates trends and risks).

BI tools work with different types of data efficiently: third-party and in-house, real-time and historical, structured and unstructured.

Power BI is an easy-to-scale platform for self-service Business Intelligence and data visualization developed by Microsoft in 2014.

A data lake is a centralized storage for huge amounts of raw data. It simplifies further data processing and management.

While a database keeps real-time data required for app functioning, a data warehouse is a repository for well-structured historical data and one of the BI key components.

With the algorithms of artificial intelligence and machine learning, it's possible to automate and upgrade big data analysis. At the same time, by learning big data patterns, the algorithms get trained and become smarter.

Artificial Intelligence and Business Intelligence are separate technology branches that may sometimes complement each other. While the first notion refers to the attempts to make machine intelligence as smart as human one, the second one targets the enhancement of the decision-making process of business managers.