Today, in 2025, data has become a valuable resource for most businesses. By processing this data, it is possible to gain valuable insights that can help make informed decisions, optimize processes and improve business efficiency. However, not all organizations need complex data processing platforms right away. Let's explore when an organization should consider implementing such solutions, what options are available, what they consist of and how to choose the right one.
When does an organization need a data processing platform?
A data processing platform is not just a set of tools; it is the foundation for decision making in modern business. It allows you to:
-
Automate routine processes, freeing up staff time.
-
Gain insights from data that might otherwise be overlooked.
-
Predict future trends and make decisions based on data rather than intuition.
-
Scale the business by processing increasing amounts of data without compromising performance.
Even small businesses can benefit from data. For example, by keeping track of customer information in an Excel spreadsheet, they can identify those who have not visited for a while and send them personalized offers. However, as a business grows and the volume of data accumulated increases, relying on Excel alone becomes insufficient. Here are some signs that it may be time for a company to consider a platform:
-
Increasing data volumes: When data comes from multiple sources (CRM, website, marketing channels) and manual processing takes too much time.
-
Need for automation: If staff spends hours on routine tasks that could be automated.
-
Need for deeper analytics: When simple reports are no longer sufficient and there is a need to forecast demand, identify patterns or optimize processes.
-
Growing number of analysts: When there are multiple teams working with data across the organization and processes and tools have to be standardized.
Levels of data analysis
The evolution of data processing can be divided into several stages. Not all companies go through these stages sequentially; some may move straight to complex solutions if they have the resources and the task. What are these levels?
-
Descriptive
At this stage, data is used to answer the question, "What happened?" For example, a coffee shop collects information about customers and visits to understand how many services were provided in a month. Tools: Excel, Google Sheets. -
Diagnostic
This is where data helps answer the question "Why did this happen?" For example, discovering that sales growth is linked to a successful advertising campaign. At this stage, companies start using BI systems (Power BI, Tableau) and move from Excel to SQL and Python. -
Predictive and prescriptive
At these levels, data is used to make predictions – What will happen? – and recommendations – What should be done? For example, forecasting customer base growth or determining how to optimize the marketing budget. This requires data processing platforms and a team of specialists. -
Autonomous analytics
This is the highest level, where AI-based systems independently analyse data and provide solutions. For example, banks use scoring systems to assess the creditworthiness of customers.
Types of Data Processing Platforms
Data processing platforms vary in functionality and complexity. Here are the main categories:
- Batch processing platforms: Apache Hadoop, Apache Spark. Suitable for working with large volumes of data accumulated over a period of time. Used for log analysis, reporting and transaction processing.
- Stream processing platforms: Apache Kafka, Apache Flink. Process data in real time. Used for transaction monitoring, data analysis from IoT devices and content personalization.
- Storage and analytics platforms: Amazon Redshift, Google BigQuery, Snowflake. Designed for storing structured data and running complex queries. Used for business analytics and historical data storage.
- Machine learning platforms: Provide tools for developing and deploying ML models. Commonly used are TensorFlow, Databricks. Used for forecasting, recommendation systems and image analysis.
- Hybrid platforms: Apache NiFi, Cloudera Data Platform. Combine the capabilities of batch and stream processing, analytics and ML. Suitable for complex ETL/ELT processes and integration of data from disparate sources.
Components of Data Processing Platforms
A data processing platform is a complex ecosystem of tools and technologies that work together to collect, store, process and analyse data. It can be likened to a pipeline, with each stage having a unique function, and after passing through all the stages, raw data is transformed into valuable insights.
- Data sources: It all starts with data sources - the points from which information enters the system. Sources can vary widely, from CRM systems and ERP programs to log files, IoT devices and SaaS applications.
- Data collection and integration tools: Once the data arrives from the sources, it needs to be collected and sent to the system for further processing. Tools like Apache Kafka are great for real-time data streaming, while Apache NiFi is great for extract, transform, load (ETL) processes.
- Data storage: Collected data needs to be stored somewhere, and this is where data warehouse come into play. There are several types, from distributed file systems such as Hadoop HDFS to cloud solutions such as S3 object storage.
- Data processing and transformation tools: Raw data is rarely ready for analysis. It needs to be cleaned, transformed and structured. Tools such as Apache Spark allow both batch and real-time processing.
- Analysis tools: Once the data is ready, the analysis phase begins. Analytical tools help to extract useful insights from the data. BI systems such as Power BI or Tableau allow you to create visualizations and reports.
- Visualization and reporting interfaces: Data is of little use if it cannot be presented clearly. Visualization and reporting interfaces allow the creation of charts, dashboards and other visual constructs that are easy to interpret.
- Machine learning tools: When an organization needs to go beyond analyzing data to predicting trends, machine learning tools come into play. TensorFlow and Scikit-learn enable the creation, training and deployment of ML models.
How does it all work together? Imagine you run a chain of stores. Sales data comes from your CRM system, website logs from servers, and inventory data from IoT sensors in the warehouse. Collection tools, such as Apache Kafka, transfer this data to a storage, such as S3. Apache Spark then cleanses and structures the data, and Power BI creates dashboards that show you which items are selling best. If you want to forecast demand, TensorFlow helps you build a model that predicts how many items you need to order for the next month. All of this is the work of a computing platform.
The choice of components depends on your business goals. If you're working with large volumes of data, you'll need distributed systems such as Hadoop or Spark. If speed of processing is important, consider ClickHouse or Apache Flink. For real-time analytics, consider Kafka and Power BI, and for machine learning, TensorFlow or SageMaker. The most important thing to remember is that the platform should be flexible, scalable and able to grow with your business.
Choosing a data processing platform
The choice of platform will depend on your organization’s tasks, data volume and resources. What should you focus on first?
- Data type: Structured data is better handled in warehouses (Redshift, BigQuery), while unstructured data is more likely to be processed in Hadoop or Spark.
- Data volume: Cloud solutions are suitable for small volumes, distributed systems for large volumes.
- Processing speed: If real-time processing is required, choose streaming platforms (Kafka, Flink).
- Budget: Cloud platforms are easier to set up, but can be more expensive than on-premises infrastructure if not managed properly.
- Integratability: Make sure the platform supports integration with your current systems..
Options in Russia
In Russia, both foreign and local solutions are available. For example:
- Cloud Platforms: Cloud4Y.
- Boxed Solutions: Domestic analogs of Hadoop, Spark, and other tools.
- Integrated Platforms: Solutions from Russian vendors that combine storage, processing, and analytics.
Building a data processing platform
There are two approaches. The first is to build your own infrastructure in-house. This provides complete control and the ability to customize for specific business needs, which is particularly important for large organizations with high security requirements. However, this approach involves significant initial investment and long implementation times.
An alternative and more common option is to use cloud services. This allows the platform to be deployed quickly with minimal up-front costs, while the provider supports the infrastructure. This solution is ideal for organizations with limited budgets or fluctuating workloads, but implies dependency on an external service provider.
The choice depends on the specific needs of the organization. Large companies with specific long-term requirements may find it more beneficial (but not easier) to develop their own infrastructure, while small businesses and start-ups may find cloud solutions more efficient.
