Apache Spark Development Services

What business problems does Apache Spark solve that our current setup cannot?

Using Apache Spark as a data-processing framework helps detach the project from the constraints of existing hardware and infrastructure, letting you focus on building efficient, reliable data pipelines by distributing data volumes across a cluster. It also combines a number of different functions (streaming, SQL queries, machine learning, etc.) into a single platform, reducing the need to integrate and manage separate complex tools in your workflows.

If you have specific business problems you are looking to solve or are considering Spark for, we can help with the decision-making process. Schedule a meeting, and we’ll talk about what objectives Apache Spark and our technical expertise can help you achieve.

What drives the cost of Apache Spark development, and what typically causes overruns?

The primary cost drivers for Spark development are the system’s scope and complexity, which dictate secondary factors such as required team size, expertise level, and project timeline. Budget overruns can stem from a lack of Spark expertise that leads to inefficient code and poor resource management, as well as from unrealistic initial estimates.

SysGears consultants and business analysts can help you more accurately estimate and plan the development budget for your Apache Spark project, and our engineers can ensure quality code.

Who on our side needs to be involved, and how much of their time will it take?

This depends on the cooperation model we select at the beginning of our partnership. In outsourcing, we work with responsible executives to discuss key development decisions, share updates on our work, and ensure we stay aligned with your goals and priorities if they change throughout the project. In dedicated teams or staff augmentation, our experts follow your project structure and work directly with your PMs and team leads. If you have specific needs for your company’s involvement in the development process, we can adapt our services to your project’s unique requirements.

Who owns and maintains the pipelines once the engagement ends?

We can offer ongoing post-release maintenance from our developers to support your data processing system’s long-term health, modernize it as needs evolve, and expand it with new features. Alternatively, if you don’t need further maintenance assistance, we can leave it in the hands of your in-house team, along with proper handover and technical documentation.

Do we actually need Spark, or would a simpler setup handle our volume?

It depends on how much data you’re looking to process, how you’ll process it, and your long-term goals for the project. We would be happy to discuss your data volumes and processing goals with you and arrive at a technology stack that presents the optimal solution, whether it is Apache Spark or a simpler framework. Contact us, and our consultants will help you find the best way forward.

We’re on legacy Hadoop — what does another year of waiting cost us?

While Hadoop MapReduce is less commonly chosen for new data-processing projects, it will likely remain sufficient for existing systems whose processing requirements are stable and where its performance characteristics continue to meet business needs.

If you expect significant processing bottlenecks in the near future, transitioning to Spark could be one potential solution. Our consultants can assess your current setup, identify current and potential future issues based on your goals and priorities, and help you determine whether you need an upgrade.