Next Steps for Java Developers: Navigating Career Advancement

Java developers of different levels often wonder about opportunities to gain new experience without making a drastic change in their expertise but building upon their existing Java knowledge. According to recent research, Java remains one of the most widely used programming languages, and this trend is evident at SoftServe as well. Java offers vast opportunities for professional development, enabling engineers to work with diverse frameworks, database types, unique architectures, high-performance systems, collaborate with clients, and engage in business domains specific to clients.

However, if your interests expand beyond Java itself and involve architectural solutions, client interaction methods, Big Data, AI/ML, or platforms, and you have the desire to master new technologies or shift to different directions, then why not explore further? In the following, we will discuss some of the most popular development directions in Java that we have observed.

Platforms: Working with platforms might seem simple at first, as it involves using ready-made elements offered by the platform and adapting them to the specific project's needs. However, in reality, it is a complex engineering task, which includes the:

Platform work - configuring the platform, building integrations, etc.
Developing additional elements - some of which may need to be created from scratch for a particular project.
Close collaboration with the business.

Platforms are rapidly evolving, providing increasingly wider functionality, making them interesting for companies. We see significant demand from clients, while the talent market for these platforms is not saturated (hence, less competition).

Let's consider two platform categories:

iPaaS (Integration Platforms as a Service - Mulesoft, Apigee, Dell Boomi): As defined by Wikipedia, System Integration (or Data Integration) involves routing data from various sources (databases, systems, mobile/web applications through APIs, etc.) into a single system/platform within an organization or across organizations.

Integration platforms come in handy when:

A client adopts a vertical scaling approach and approaches us with a request to integrate a system they already use with a new one (e.g., integrating Workday with SAP SuccessFactors).
A client adopts a horizontal scaling approach and develops their system, adding new modules to it. There is a need to quickly combine these modules for data handling, transformations, data synchronization, etc.

Certainly, using an integration platform is not the only option. One could also opt for USP solutions, such as Apache Kafka. However, let's be honest, configuring such a platform can be quite challenging, even for very experienced engineers. Using an integration platform makes this process much faster and simpler.

In simple terms, each integration platform is like a set of blocks from which you build what you need. Additionally, it provides critical auxiliary services (partially covering USP, security, and forming the data layer).

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How to transition?

Moving into this area is possible regardless of your current focus. However, based on our experience conducting retraining programs in our company, I can say that the higher the engineer's qualification, the deeper they can understand the platform's more advanced possibilities and options.

Essential topics to learn include:

Object-oriented programming languages
Specifics of the integration platform (our engineers start with knowledge of one platform, but for promotions to tech lead and beyond, they need to be able to work with at least two platforms)
REST/SOAP web services
Integration patterns
Data exchange formats

In the next stages, you will also need to understand:

Scripting language (JavaScript or Groovy)
Security principles
Database design
Cloud computing

Certifications to pursue:

Dell Boomi Associate Developer Certification
Professional Developer Certification
MuleSoft Certified Developer

The next step is more advanced training focused on the project specifics, combining product and technical aspects, and takes up to a month.

In a few months, a developer will be ready to work on real integration projects under the guidance of a more experienced professional.

Salesforce

Salesforce is a cloud-based CRM system used for managing business processes related to sales, customer service, and digital marketing. It consists of several separate products, including Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, Financial Services Cloud, and Health Cloud.

In 2007, Salesforce launched Force.com, the first PaaS platform for developing and deploying applications within the Salesforce infrastructure. In 2018, it was renamed Lightning Platform.

When it comes to working on this platform, there are two roles:

Consultants - they study the client's needs and plan how Salesforce can meet them.
Technical experts (engineers, architects).

One unique feature of Salesforce is that even technical experts need to be client-facing. Therefore, those who want to try this direction must possess strong communication skills, as knowledge of the technology alone is not enough.

This is because all platforms impact their users' businesses, but Salesforce directly configures the sales process and many associated processes, affecting revenue. Its functionality is used by COOs, CMOs, and other business stakeholders. As such, constant communication with them is necessary to understand their needs, receive feedback, and respond promptly. Moreover, one must understand the business and speak its language.

The pace of work is highly dynamic, and another standout feature is that Salesforce is written in Apex (a Java-like language used to implement complex business logic), with the platform taking care of routine tasks. Thus, thinkers are needed, not just doers, individuals who are willing to learn, find ways to overcome challenges and adapt quickly.

For developing the interface, the modern JS Framework, Lightning Web Components, compliant with es6 standards, is used.

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How to transition?

If you know the basics of Java and progress step-by-step, you can aim for a Junior position. If you already have 2-3 years of practical experience as a developer, you can reach a Middle position.

Certifications to pursue:

Platform Developer 1 for the entry level, followed by corresponding levels of the same certification.

Transitions to Salesforce happen both among experienced developers (we have a retraining course for such individuals, where in two months, they become sufficiently acquainted with the platform to obtain their first certification and apply for a Middle Salesforce Developer position) and newcomers.

Big Data

Big Data engineering became relevant about 10 years ago, and what is happening in this field today? In short, it is gaining momentum rapidly. New data sources keep emerging, and the market is growing swiftly. According to Fortunebusinessinsights, the market's value is expected to reach $116 billion by 2027. Many organizations strive to be data-driven, meaning they make informed decisions based on real data and analytics.

Undoubtedly, this impacts the work of engineers. New challenges arise, such as processing data quickly (receiving and processing large volumes of data in tight timeframes, preferably in real-time while the data is still relevant) and working with more data sources that have different interfaces for accessing them.

Consequently, the demand for experts is increasing. According to QuantHub, big data engineering is the fastest-growing IT specialty among all IT professions. The same trend is observed in the local market.

Data Engineering Directions

Working with Big Data involves four key stages. Opinions vary on whether one engineer should handle all processes or whether each process should have a specialized expert. In any case, all fall under the same expertise, so it's essential to understand each of the processes listed below.

Database Engine Selection

Selecting the right database engine is a complex compromise based on various factors such as scalability properties, transactional support, flexibility of the model, data access patterns (operational point queries or analytical large scans), availability, fault tolerance, support for isolation and consistency levels, graph, and geolocation structures, full-text search, the ability to filter non-key attributes, support for grouping and entity aggregation operations, query languages like SQL, cloud deployment capabilities, market prevalence, maturity, community support, usage cost, and much more.

Data Modeling

Modeling data is often a non-trivial task that involves projecting real entities and processes onto the chosen database engine. There are two main parts to consider—relational and non-relational modeling.

In the relational world, modeling benefits from well-established methodologies and numerous patterns for inheritance. However, there are many trade-offs: trying to apply a highly normalized approach to avoid duplication and problems with abnormal updates, or creating denormalized structures to avoid joining operations. The former approach allows for the best operational model, while the latter is better for analytics, i.e., queries scanning large volumes of data.

For highly normalized structures, there are various variations and methodological directions, such as Data Vault and Anchor Modeling, among others. On the other hand, analytical models have their methodology with well-established approaches for multi-dimensional modeling based on star-like structures.

Non-relational data structures, which are oriented towards scalability and storing large volumes of data, significantly differ in design. Here, the design of real entities and processes becomes less crucial, and the specific data queries to the non-functional constraints of the data engine take precedence. Each new query often requires a new data structure tailored specifically for it. In other cases, the non-relational engine might not be capable of delivering what is asked.

Building Integration Processes

This is the most labor-intensive process, which can account for up to 70% of all efforts in building an analytical project.

As mentioned earlier, with the pace of data growth worldwide, the ability to process large volumes of data in the shortest time becomes increasingly critical. Therefore, experts involved in this process must understand concepts of horizontal scalability, parallelization, and principles of distributed computing.

However, functional aspects should not be overlooked. Ensuring data quality, compliance with openness standards and security, tracking data lineage, and other requirements are essential from a data processing perspective.

Regarding integration challenges, there are numerous ones. These include incremental data extraction from sources through various access interfaces, data cleansing, deduplication, data reconciliation, data enrichment through external data dictionaries, auditing all operations, and orchestrating all processes with the necessary sequence and interdependence.

There is a division between real-time integration and batch data processing. Real-time processing is becoming more popular, and the number of frameworks that aim to facilitate developers' work is increasing daily.

Furthermore, there is a not-so-obvious compromise between data quality control and speed of data delivery to the recipient: the faster we deliver the data, the less time there is for thorough verification and data enrichment. Therefore, a deep understanding of the technology-provided toolkit and its correct application in conditions of rapid parallel data pouring is often a defining characteristic of an expert.

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Data Presentation and Visualization

Data visualization is another interesting direction in data engineering that requires close collaboration with business representatives to ensure data is presented in a way that enables effective decision-making.

Here, besides having a sense of aesthetics and skills in effectively presenting data to users, it's essential to have the desire to delve into business processes to transform data into real recommendations for optimizing operations and increasing profitability.

Where to Start

One way to delve into the world of Big Data is to study the services and approaches of one of the cloud providers. If you have a specific potential project on a particular cloud provider (GCP, Azure, AWS), you can start with specialized online courses offered by each of them. Cloud certification preparation courses typically provide material that is clear and well-structured, helping you grasp the theory and gain some hands-on experience through lab exercises.

However, in addition to that, we highly recommend training that explains the general concepts of distributed systems (databases and computations). This will give you a more comprehensive approach and avoid oversimplifications and misunderstandings.

Representative training in this area can cover Spark (an example of a system supporting parallel computing) and Cassandra (an example of a NoSQL distributed database).

Conclusion

Overall, transitioning from one competency to another, though challenging and sometimes time-consuming, is entirely feasible. The key is understanding where your desire lies regarding career development. Perhaps you can try creating your small project using the chosen technology or take a practical training course. Work with a mentor or acquaint yourself with the theoretical aspects of a programming language that interests you from available open sources. Certainly, changing directions requires significant effort as you will need to learn many new things, but it also opens up opportunities for professional growth.

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