Databricks: The future of big data management

Data explosion through digitization: challenge and opportunity at the same time

As a company's degree of digitization increases, the amount of existing data generated by the various systems grows analogously. Different software solutions are often used in the various departments of a company, such as PMS, CMS, CRM, inventory management systems, accounting software and more. All of these systems generate data, which, however, is available in different file formats. In principle, this is not tragic. However, if you want to use this data for analysis, evaluation and reporting, the data must first be prepared. There are two different methods for this: data warehousing and data lake.

What is a data warehouse?

Basically, all data from a company's various systems is collected centrally. In a data warehouse, data is available in a structured and consistent manner on a central system. This enables easy access to data.

A data warehouse is designed in such a way that data extracts are possible using data access tools. This means that the data can be analyzed according to individual specifications and patterns. It is precisely these analyses that form the basis for determining important operational KPIs.

Design and function of a data warehouse

When it comes to the architecture of a data warehouse, there are four different areas: Source Systems, Data Staging Area, Data Presentation Area, and Data Access Tools.

The first step is to provide all data obtained from the various systems. The extraction, structuring and transformation of the data is carried out by the staging area of the data warehouse. This also brings the data to the data warehouse database. This database is the so-called Data Presentation Area. Data access tools are used to access the stored data at various levels.

A data warehouse helps to separate analytical and operational systems and allows controllable data analyses in real time. These analyses range from resource identification, cost calculation, process analysis to the determination of important key company figures and the preparation of statistics and reports.

However, a data warehouse is not only used for analysis purposes. The provision of data, as well as its harmonization and structuring, is also an important purpose of a data warehouse. A data warehouse uses data that has been recorded in databases in a structured form. However, if large amounts of data are available in unstructured form, a data warehouse is no longer sufficient. That is why, at a certain point, the data warehouse is combined with a data lake.

What is a data lake?

A data lake is designed in such a way that the storage of large amounts of data is no problem due to the high storage capacity, regardless of whether it is structured, semi-structured or unstructured data. A data lake is also capable of processing large and unstructured amounts of data. Different formats and different storage locations are therefore a thing of the past with a data lake.

Within the data lake, the data is professionally prepared and modeled in such a way that regular, automated reports can be created and ad hoc queries can be generated on logically consistent models and validated data. A data lake is best suited for analyzing and evaluating large amounts of data that are available in unstructured form.

Business intelligence and reporting

But what does business intelligence actually mean? Basically, BI means nothing more than business analytics. The aim is to obtain insights from the data available in the company to support management decisions. The evaluation of data about one's own company, competitors or market development is carried out using analytical concepts and specific software and IT systems.

With the knowledge gained, a company can optimize its business processes as well as its customer and supplier relationships. This in turn strengthens a company's competitiveness. Without evaluating the existing data, management decisions would lack any basis. The advantage of business intelligence is obvious: well-founded decisions based on large amounts of data minimize the fault tolerance of decisions.

Local or cloud-based data processing?

When it comes to the type of data processing for BI and reporting, there are two different options: on-premise or in the cloud. Cloud-based data processing is provided by third-party providers such as Amazon, Google or Microsoft and has the advantage that it is not necessary to set up and operate your own server infrastructure.

Benefits of cloud-based computing

Other benefits of cloud-based computing include:

• Scalability: Cloud computing services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) allow computing and storage resources to be scaled quickly.
• Cost savings: Cloud-based computing can reduce the costs of buying, setting up, and maintaining local servers and data centers because companies only pay for the resources they actually need.
• Flexibility: Cloud-based computing offers a variety of applications for data processing.
• Speed: Cloud-based data processing is high-performance and enables rapid data analysis and insights in real time.
• Independence: Cloud-based data processing enables access and processing of and processing of data from anywhere.
• Safety: Cloud-based data processing providers guarantee a high level of security measures to protect data.

Overall, cloud-based computing helps optimize your workloads, save costs, and gain faster insights for your business decisions.

What is Databricks?

As far as applications for data processing and processing are concerned, there are a number of providers on the market. However, one provider has recently turned out to be more and more the standard: Databricks is a cloud-based data platform based on Apache Spark and developed to make it easier to manage and analyze big data. Databricks provides an integrated development environment (IDE) and tools for collaboration and automation of data-related tasks. The platform can also be used with all major cloud providers such as AWS, Microsoft Azure or Google Cloud Platform.

What are the benefits of Databricks?

During the introduction of Databricks, the data is structured for data processing and data analysis, converted into a format optimized for queries and stored in cloud storage. Databricks ensures that the already structured data for data processing and data analysis is stored and harmonized in an optimized format. As a result, the prepared data can be combined for reports and analyses to obtain information.

Databricks Machine Learning is based on an open lakehouse architecture, i.e. the combination of data warehouse and data lake, and supports machine learning teams in preparing and processing data. The platform offers a variety of advantages for machine learning.

Benefits of Databricks at a glance

• Scalability: Databricks makes it possible to process large amounts of data and quickly scale analytics workloads.
• Flexibility: Databricks supports various programming languages, including Python, R, Scala, and SQL, so data analysts can work with their preferred language.
• Real time processing: Databricks supports streaming data processing so that data can be analyzed in real time.
• Collaboration: Databricks makes collaboration between data analysts, scientists, and engineers easy because everyone can work in a central environment.
• Automation: Databricks provides tools for automating tasks, saving time and resources.
• Safety: Databricks provides features such as access control and encryption to ensure data security.

Conclusion

Companies are using more and more systems that produce more and more data. However, companies can only really benefit from this data if they can use it correctly for analysis, reporting and forecasting. A data lake and, as a tool, the current standard Databricks are best suited for processing big data. If you'd like to learn more about Big Data, Data Lakehouse and Databricks, book your free live demo here.

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