Is it the time to adopt a modern data stack for your organization?

The world of business is changing. More and more companies are trying to make more data-driven decisions. In order to benefit from business insights, they have to establish a proper data strategy, assess old processes and create new ones. Efficient data management also requires high quality IT software and tools. What is a modern data stack and how do you select the right solutions for your organization?

When should you upgrade your data engineering stack?

You may wonder if moving from a traditional data stack is really necessary. In the last two decades, “the digital transformation” has become a real buzzword – partially because it is an umbrella term for many IT-related improvements that a company can perform. It is not only about changing the way the IT-department operates. The digital transformation alters the entire company in terms of IT solutions and business processes, but also culture and work organization. 

Usually large, older companies decides to move from on-premises infrastructure and migrate to the cloud; hence they also have to choose new data processing and management tools. This provides a good opportunity to research the market and seek out advice on the best modern data stack for the organization. Evaluating current technological solutions and selecting a new, modern data engineering stack is also recommended to companies that decide to change their business model or introduce new services or products to the market.

In general, it is better to upgrade sooner rather than later. Without a modern data stack, you lose your competitive advantage over other companies from your industry, and you can’t achieve your optimal efficiency before competitors. 

Modern means (among other things) cloud-based

For most IT professionals, one thing is quite clear – “modern software for business” means cloud-based software. It is all about flexibility and availability of your business solutions. Modern data processing tools are hosted in the cloud, so they can be accessed from anywhere through the Internet and from many devices. A modern data stack like this will also be cost-effective and scalable as cloud-based solutions usually can be leveraged in a pay-as-you-go model, which means that you pay only for the resources and services you actually use.

What is a modern data stack?

A modern data stack is a suite of tools for end-to-end data processing (from data ingestion to producing business insights or empowering applications). Such a set of IT solutions should include:

• cloud-based storage solutions (warehouse or data lake),
• a fully managed ELT (extract-load-transform) data pipeline,
  • data transformation tools,
  • data cleaning tools,
• data science platform,
• a business intelligence or data visualization platform.

But among each of these modern data stack component types, there are a number of open-source and commercial solutions you can adopt in your company. How do you choose the right ones?

Modern data stack components – how do you select the right ones for your organization?

You already know that you should be using cloud-based tools. But what are other important features of each modern data stack component?

Data ingestion and integration

First, it would be wise to consider whether you’ll be dealing with real-time data. You should pick your modern data stack based on what types of data you plan to work with. After extracting data from its source, it has to be put in its final, centralized location, where it will be available for all users that may need it. The perfect solution for your business should have built-in integrations with all of your data sources. It also needs to be easy to set up in a way that enables efficient scaling. You need a reliable tool for bringing all data from different places together.

Data storage

To be available for analytics, your data has to be stored in a centralized location, often referred to as a cloud-based data warehouse or data lake. The storage services should be scalable, but this will not be a problem if you select a cloud-based solution. Additional features of your storage should be defined for your specific company. Such solutions can be evaluated based on factors such as performance at scale, ease of use, support for unstructured, semi-structured and structured data, concurrency, data granularity and many others.

Data processing

Making data ready for analytics may be time-consuming and challenging. Your data needs to be transformed into a suitable form for its purpose. Data cleaning enables your data engineers to increase your data quality (and thus, the reliability of your final business insights). In case you want to enhance your data sets, you can perform data augmentation. Perhaps you will not use all these tools. In this case, you also should consider what is best for your organization. Analyze your business requirements and goals and seek advice from experienced professionals.

Business intelligence and data visualization

Consider carefully who will use this tool – highly skilled, tech-savvy employees or other members of the team? Are they capable of creating SQL queries, or will they need an intuitive interface? Try to choose a solution that will be easy for your experts to use. A good business intelligence tool should allow users to easily access and visualize needed data. It should be flexible and customizable. Many companies would also appreciate collaboration and sharing features.

In the end, you can’t forget about security. Data can empower a company, but being careless with business information can cost you a lot. Security is a very high cost that is often missed in calculating the total cost of the service in on-prem, which is included in cloud solutions. Most cloud tools are shipped with advanced cybersecurity solutions and ensure compliance.

Benefits of adopting a modern data stack 

Above all, a modern data stack is much more intuitive, efficient and flexible in terms of payments. Agile, mature companies turn to cloud-based solutions because they provide them with almost unlimited business flexibility, while taking away some responsibilities (like maintaining equipment, selecting cybersecurity methods etc.) they would rather be free from. Using cloud lets companies focus on their core business and being sure that their infrastructure is built by professionals.

Here are some of the most important advantages of adopting a modern data stack:

• Cost-effectiveness – modern tools for data processing often come in a pay-as-you-go model. That means that you pay for the exact amount of storage, compute power or tools you actually use. What is more important, you are not limited by the chosen solutions, because most of them are scalable, which means that you can scale up and down whenever you want. 
• No long-term commitments – before cloud-based solutions, users had to pay a lot for software. Then they often delayed (sometimes for a very long time) investing in new solutions, even if they knew that their current tools didn’t suit their businesses’ needs any longer. When it comes to on-premises, traditional solutions, they are not flexible , and you can’t simply stop using them even if your business model changes, new useful features have been developed or simply because someone made a bad choice.
• Access to the most advanced technologies – the biggest cloud-based solution providers are developing new features for their tools all the time. A modern tech stack allows you to leverage NLP, analytics powered by artificial intelligence and more. Giant IT companies are tirelessly coming up with new features to make their software the best on the market.

In today’s data stack market, you will find tools developed for non-tech organizations as well as for software houses. Every company (small, medium and large) can make data-driven decisions. Contact us and tell us more about your business goals and your requirements. We can help you choose a modern data stack to suit your needs.

Check out our blog for more details on Big Data:

• What is Big Query, and how can it support your analytics?
• Why is BigQuery replacing Hadoop for enterprise analytics?
• Optimizing Apache Spark