Big Data Management: Most Important Technologies and Applications - 【2020】
Big data technologies and applications

Big Data Management: Most Important Technologies and Applications

Big Data applications are not based on a single technology, but are basically a combination of several innovations. However, conventional technologies such as classic databases, data warehouses or reporting solutions are far from sufficient for this.

Major data technologies enable, among other things, the optimization of existing business processes with regard to the use of resources, the additional use of previously unused data sources to support processes and the development of new business models or the individualization of products and strategies. New divisions can be opened, for example, by using data to implement data-based services.

Companies and Big Data

When companies evaluate their data today, they have a wide range of customizable, real-time analysis tools at their disposal. Before implementing them, they must be clear about the respective use case, as well as the type and scope of the data. At the same time, they are inundated with a multitude of technical terms that do not necessarily facilitate a decision.

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Over time, various solutions have been developed around the widely used technical term Big Data, the meaning of which is not immediately obvious, especially to newcomers to Big Data. This is because each technology has its own purpose and special data processing function.

Data mining, BI platforms and process mining in Companies

The set of statistical-mathematical methods for pattern recognition through tasks such as search, pre-processing or evaluation is called data mining. From a technical point of view, algorithms are used to establish relationships between data. So-called Business Intelligence (BI) platforms offer methods for collecting, evaluating and presenting data. In this way, operators pursue the objectives of risk and cost reduction, as well as the optimization of value creation.

All types of KPIs (Key Performance Indicators) are evaluated, i.e. key figures such as your own production, competitors, customers or market developments. It is crucial that operators define in advance exactly what they want to investigate with multidimensional analyses. However, this has a major disadvantage, as it is often not possible to know exactly what data may be relevant in the course of the analysis. Therefore, companies are increasingly trying to include data sources as well as unstructured data in the analyses.

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In contrast to BI platforms, which essentially focus on different key figures, the large Process Mining data technology deals with a more far-reaching approach: It is the analysis of processes that are considered to be end-to-end and that exactly reflect reality. This means that Process Mining can be used to visualize complete digital processes in a wide variety of variations. Based on the knowledge gained, it is now very easy to identify weak points in real time.

Another advantage: operators do not have to get into a corset of predefined questions beforehand. Because Process Mining provides an unbiased view of a company’s actual processes. In this way, companies can make optimization decisions and achieve a quick return on investment (ROI). Compared to BI, Process Mining provides important information about when, where and why these problems occurred in the first place.

Big Data Technology Categories

Depending on the specific requirements of the project, different architectures and combinations are possible. Four categories are distinguished as orientation:

  • Standardized analyses are suitable for applications with significantly lower requirements in terms of time and data diversity.
  • In-memory technologies are particularly suitable for very large data evaluations.
  • Hadoop solutions are recommended for a wide variety of data formats. Hadoop is open source and capable of storing and processing a huge volume of differently structured data. The ability to scale seems almost unlimited.
  • Complex event processing and transmission is suitable for situations where data needs to be captured and evaluated as it is created.
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Classification of Big Data technologies

Big Data’s complete solutions are again divided into individual layers. The subsequent layers mark the direct path from raw data to business-relevant results:

  • Data management
  • Access to data
  • Analytical processing
  • Display.

These are accompanied by the following layers:

  • Data integration
  • Governance and data security.

These so-called flanking layers are intended to ensure that raw data is incorporated into a company’s existing standards.

Guidance in finding appropriate technologies

Today there is a specialized or customizable solution for almost every application. When implementing a Big Data technology, users must always first obtain clarity about the type and scope of their data. The following questions support the identification of the specific need:

What data is available in the company? Is it sufficient if this data can be evaluated in the most flexible way possible? Or should ad hoc analyses also be carried out?
Who will ultimately have to work with the technology?
What are the concrete needs of the users?
Where is the data stored? Mainly from relational databases? Or is it necessary to use unstructured data sources as well?
Does the application require very high processing speeds?
Does the application require fast storage and easy retrieval of large amounts of data?
Does the application also include data from social networks for continuous Footprint analysis?

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Selection of some Big Data technologies

Companies are increasingly storing, processing and analyzing data on a large scale and generating their added value from it. The following Big Data technologies cover a large part of the application scenarios for companies:

Hadoop

The open source framework for parallel data processing in highly scalable server clusters Hadoop is especially suitable for evaluations where complex analyses must be performed.

Cloudera

A complete portfolio of proven open source applications that can be easily installed and managed in a web interface using Cloudera Cluster Manager Companies can rely on proven solutions and can flexibly integrate new, data-intensive technologies into existing processes.

Apache Hive

The data warehouse for Hadoop. Apache Hive moves data from relational databases to Hadoop using the SQL dialect HiveQL. The most important functions are the summary, query, and analysis of the data.

Impala Cloud

A scalable and distributed data recovery tool for Hadoop. Benefits: Real-time queries without having to move or convert the data.

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MongoDB

One of the leading NoSQL databases in the open source area. The general purpose database allows dynamic development and high scalability.

Pentaho

One of the leading global BI platforms. Consolidation of proven individual solutions into a complete framework. Pentaho is modular, has an open architecture and can be easily integrated into existing IT environments thanks to its many interfaces.

Infobright

The column-based database offers more flexibility with effective data compression. It is especially suitable for processing large amounts of data.

Apache Spark

A parallel open-source framework for real-time analysis that ensures the rapid processing of large amounts of data on clustered computers

Splunk

The technology is particularly well established in the field of fingerprinting and allows the monitoring and analysis of clickstream data as well as customer transactions, network activities or call records.

Apache Storm

A fault-tolerant and scalable system for real-time processing of data flows. Apache Storm is part of the Hadoop ecosystem and operates independently of programming languages.

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