How to Choose a Tech Stack for Data Analytics Platform Development  

Understanding Your Requirements 

Data analytics platform is a generic term and the linchpin for using analytical methods, from classic data warehousing to big data solutions to the analytical platform (advanced analytics architecture). It is therefore the technological basis for data governance, engineering, data analytics, and advanced analytics (predictive and prescriptive analytics), among others. Well-known services for analyzing data include Apache Kafka, NiFi, Hadoop, Azure Synapse Analytics, and Microsoft SQL Server. They work together as required for the different tasks to analyze data and visualize the results or create forecasts.  

A data platform therefore fulfills several core requirements:  

• Control and manage company data (e.g. customer data, financial data, marketing and inventory metrics, etc.) in an accurate, complete, secure, and understandable form. 
• Monitor company data in real time and create analyses and reports. 
• Integrate different data sources and data types for joint processing and presentation.  

A well-architected platform consists of various analysis modules that provide a quick overview of risks, infrastructure requirements, control weaknesses, and optimization potential: 

• Dashboard visualizations for key business processes 
• Process analyses and IT application controls for relevant standard processes 
• Separation of duties and authorization analyses based on transactions 
• Determination of performance-oriented key figures via business processes 

The variety of frameworks and technologies available in the market can be confusing, and choosing the wrong stack can cost much more than losing investments. Poor performance, lack of scalability and insufficient security measures are just some of them. Therefore, before you start writing the code for the future data analytics platform, you must thoroughly review your project requirements. You may find it helpful to answer the following questions: 

1. What are the specific pain points or challenges you currently face that a data analytics platform could potentially address?  

Tip: You need to clearly identify the business problems we aim to solve, whether it’s improving financial forecasting, streamlining reporting processes, or gaining more informative insights into customer behavior. 

2. How will a data analytics platform align with and support your overall business strategy and goals? 

Tip: Any investment should be tied to our long-term objectives, whether it’s increasing revenue, reducing costs, or enhancing operational efficiency. 

3. How well will the proposed platform integrate with the existing technology landscape? 

Tip: It’s essential to consider compatibility with the current systems, databases, and applications to ensure seamless data flow and interoperability. 

4. What are the deployment options for the platform – on-premises, cloud-based, or hybrid? 

Tip: Each option has its own cost implications, infrastructure requirements, and potential risks that must be evaluated against our specific needs and IT strategy. 

Before reviewing the tech stack, let’s examine the data analytics platform architecture. 

Then, data is processed when uploaded to the data analysis platform. There are two most well-known data processing frameworks: Apache Kafka and  Apache Spark. 

In analytics, companies process data primarily in two ways: batch and stream processing, where Apache Kafka is used as a stream processing engine, and Apache Spark is a distributed data processing engine. 

• In batch processing, a single workload can handle processing a giant amount of data. 
• In stream processing, you process small pieces of information in real time continuously. 

Both Kafka and Spark can accept data that is unstructured, semi-structured, or structured. These tools can help you aggregate data from databases, enterprise applications, or other streaming sources. The supported data formats may include XML, YAML, JSON, plain text, and others. 

As data analysis helps businesses make smarter decisions by finding meaningful insights into their data, is inextricably linked to business intelligence. BI tools help to: 

• Consolidate and aggregate data from your different data sources 
• Visualize data in a clear and concise manner 
• Analyze data to help make decisions 

Among the business intelligence solutions for data analysis, the following are very common: 

1. Power BI is a user-friendly data analytics tool that lets you model and visualize data without extensive coding. The interface is built with UI/UX design principles in mind, with familiar menus and visuals. You can link different data tables, create entity-relationship models, and layer on additional data like dates or regions.  

Despite its simplicity, Power BI enables advanced analysis by filtering across connected tables and building interactive visualizations. It’s an accessible way for businesses to derive insights from raw data. 

2. Tableau allows connecting and blending data from multiple sources, including linking tables within or across databases. Its data blending feature handles varying data granularities.  

Tableau creates a flat data table from the linked sources for analysis and visualization. It works best with underlying data in a relational structure for smoother data modeling within Tableau. In essence, Tableau centralizes diverse data sources, blends them as needed, and enables analysis/visualization, aided by a relational data architecture. 

We have already covered the nuances of the differences between Tableau and Power BI, so check the dedicated article to learn more. 

A tech stack for analyzing data should help improve applications and also provide guidance for developers and support them in their work. In general: 

• Choose a tech stack that the development team is familiar with. 
• Consider the individual requirements of a project, application or team. 
• It helps to choose a stack with tools that have a large user community, as this allows problems to be solved more quickly. 
• The tech stack should be future-proof and the components used should not lose their support in the near future. 

Three famous ready-made tech stacks include: 

1. LAMP (Linux operating system, Apache servers, MySQL databases, and the PHP programming language) 
2. MEAN (MongoDB database, Express.js server, AngularJS framework, and Node.js runtime) 
3. MERN (the difference between MERN and MEAN stacks is that MERN uses React as a web framework instead of Angular) 

Now, let’s look at both sides of a classic tech stack in more detail. 

Frontend In Tech Stack 

The frontend refers to the user interface and the tools used for design and interaction with users. The following technologies are mainly used in the frontend area: 

• HTML: HTML forms the backbone of the platform’s UI, structuring the web pages where data visualizations, dashboards, and control elements are displayed. 
• CSS: The stylesheet language is responsible for the layout of a platform. CSS styles structured HTML elements to ensure the platform is visually appealing and user-friendly. It handles layout, colors, fonts, and overall aesthetic. 
• JavaScript: JavaScript is a programming language for implementing dynamic elements. Interactive charts and data filtering options can be used without needing to reload the page. 

In addition, frontend frameworks and libraries are integrated into the tech stack. These help developers to write code more efficiently. Frameworks such as Angular, React, or Vue.js provide ready-made components and functions for this purpose. 

Backend In Tech Stack

Backend development tools of the tech stack bring together all technologies that are responsible for communicating with data analytics platforms and with which end users do not interact. In addition to the infrastructure itself – servers and cloud services – the following components belong to the server-side tech stack: 

• Programming languages such as Python, Ruby, Java or PHP. A web framework such as Django (Python), Ruby on Rails (Ruby), Spring (Java), or Laravel (PHP), provides programmers with a structured environment. 
• Databases such as PostgreSQL or MongoDB.
• Cloud-based services such as AWS, Azure, and Google Cloud. Built entirely in a cloud like AWS, the serverless stack abstracts the server side. AWS Lambda and alternatives like Google Cloud and Azure Functions execute code in response to events and automatically manage the compute resources required by that code.

Security and Compliance  

Scalability and performance considerations are only one side of the coin, knowing how to protect this data is the other. Companies often face major challenges related to cybersecurity: 

• Huge amounts of data collected from various (sometimes unauthorized) sources 
• Traditional databases with limited storage and processing capacity can’t keep up with the latest database hacking techniques 
• Poor data quality – e.g. due to manual errors 
• Lack of support in building a security-first culture in the company 

In response to some of these challenges, in recent years companies have begun to prioritize security in their technology choices. Often, even a single insecure tool can literally open the door to attackers – the cost of data breaches was tremendous $4.45 million in 2023, which is a 15% increase over 3 years. 

GDPR requires that you obtain consent from users before collecting and using personal data. When you consider the approval flows between third parties, this process can be a real challenge. Therefore, you must put security and compliance measures first when deliberating how to choose a tech stack for a data analytics platform.  

• How can you protect your data in the event of a ransomware attack? 
• How do you ensure that your backup data is not tampered with and is always available for recovery? 
• Can you reliably protect your data from accidental deletion, bit rot, and tampering? 
• If a hardware failure occurs, can you recover lost data from the affected storage, server or site? 
• How do you prevent unauthorized platform access and protect the integrity of your data? 

Data analytics services usually do not only provide the technical basis to prevent potential data leakages through targeted data analysis, but also give a helping hand as holistic partners.  

From the initial consultation to installation and license sales, there are many factors to consider. In addition, specialist knowledge in the field of latest security technology trends and compliance with regulatory requirements (e.g., GDPR, HIPAA) are essential. Only then will nothing stand in the way of robust protection measures. 

Depending on the resources available, you can choose between on-premise, cloud, or hybrid. These considerations include the need for security and compliance, the price of the various platforms, the skills and tasks you want to keep in-house and those you would source from vendors, and more. Once you have the basic requirements in place, it’s time to vet and test potential providers. Here are a few available options: 

• Assess open-source options. Start by evaluating open-source technologies like Python, R, and Apache Hadoop. These can significantly reduce upfront costs while providing robust functionalities for data analytics. However, a big disadvantage of this method is publicity. When a vulnerability occurs, it immediately becomes known – and primarily to attackers who monitor such gaps.  
• Evaluate total cost of ownership (TCO): Every product and every service has its price – but that is not just the purchase price that you pay when you buy it. The concept of total cost of ownership (TCO) is based on a holistic view of not only the acquisition costs, but also all ongoing direct and indirect costs for a deliberate resource allocation. However, it should be noted that some factors remain uncertain and are difficult to add to the purchase price with sufficient precision. 
• Leverage cloud services. Cloud platforms like AWS, Google Cloud, or Azure provide scalable, pay-as-you-go infrastructure for a robust decision-making framework. This approach minimizes capital expenditure and allows for flexible scaling based on demand. But customers often have minimal to no control over cloud-based infrastructure and are highly dependant on external service providers. 
• Consider a tailor-made development. If off-the-shelf solutions don’t meet all your needs, assess the feasibility of custom development. For example, a tailored customer data platform development can be initially developed with narrow-targeted functions and save costs on potentially unnecessary built-in features of standard solutions. 

Conclusion  

The enormous growth in data sources and volumes as well as the different data requirements of different users pose a major challenge. With so many different types of software tools available, it’s easy to get lost. It’s important to emphasize that choosing the right tools depends on your company’s individual needs and goals, so you won’t need all of them.  

Data is the new gold, and companies that understand how to use it effectively will undoubtedly be the ones that succeed in the digital economy. Have a non-binding talk with the Lightpoint expert to weigh all the opportunities and assemble the best fit for your use case.