Unlocking the Power of Big Data Using Data Engineering Services

Jovica Turcinovic Date 27-Apr-2023
Unlocking The Power Of Big Data Using Data Engineering Services

Table of contents

    Data is now one of the single most valuable assets businesses have at their disposal. It's an asset often compared to oil for both its intrinsic raw value and its potential to be refined and processed into something much greater. What data engineering services provide for organizations is the processes, tools, and expertise necessary to capture and refine that data in order to enhance their data science capabilities.

    Data engineers achieve this by providing a foundational infrastructure of hardware and software for businesses to build their data services from. Starting with data collection from integrated services, IoT devices, and internal sources — data engineering services capture, process, and store data to make it available for data scientists and analysts.

    Our Data teams are made up of experts in data science, data engineering, and data analysis. Each team member has an in-depth technical knowledge and experience that can meet any modern business challenge. Using experts in each of these disciplines allows us to assist businesses to create better strategies and make better decisions using their data.


    Here, we outline how we achieve these goals using data engineering services within organizations. We take a look at what a data engineering company can provide for teams, the crucial skills necessary to manage big data, and what can be done by data engineers to transform the way you use and understand data within your organisation.

    The Role of Data Engineering Services Within Organisations

    The value data engineering provides to businesses comes from both its ability to collect, store and manage its raw product data and its ability to clean, filter, and refine that product to even greater value. At its core, data engineering is the hands-on practical side of interacting with data while scientists and data analysts typically use this output to build models and report on current trends.

    A modern explosion in data collection and processing capabilities is now driving huge opportunities for businesses everywhere.

    These opportunities primarily come from the abilities of engineers to capture source data from IoT technologies, cloud services, internal databases, data lakes, data warehouses, and processing systems using modern resources.

    The other side of that coin is the significant challenges created by incorporating diverse data sources and formats at an immense scale. 

    A gigabyte of storage used to be an available resource for data engineers. Now, more commonly, it's a relatively small unit of measurement on the way to terabytes of data.

    A significant part of the role of a data engineer involves creating an information architecture that rises to these challenges. For just data storage alone data engineering services rely on a broad array of technologies for a diverse suite of use cases. Some of the most common data storage solutions we deploy for organizations include:

    • Relational databases to serve structured data. MySQL, PostgreSQL, and Oracle are often used to handle easily characterized data.
    • NoSQL databases are well suited to storing semi-structured and unstructured data collected by systems. MongoDB, Apache Cassandra, and Apache Hbase are regularly used for this reason.
    • We regularly configure data warehouses such as Amazon Redshift, Azure Sunapse, and Google BigQuery to house processed data for use in large-scale data science work.
    • Data Lakes such as Amazon S3, Azure Data Lake Storage, and Google Cloud provide services that we deploy to house raw incoming data at a vast scale.

    With data collection and storage fully configured, we next have to turn our attention to building the pipelines and workflows necessary to process and filter that data for further analysis. The infrastructure to do each of these at scale with the performance necessary for data science is far from trivial.

    The most common tools we deploy to build processing pipelines and data workflows include:

    • Apache Spark, Hadoop, Kafka, and Spark
    • AWS Lambda, EC2, ECS, and CodePipleline
    • Azure Data Factory, Functions, and Databricks

    Data engineers use these tools alongside many others to process and refine data with the accuracy and compliance standards necessary to serve modern businesses worldwide.

    Data Engineering vs Data Science

    The roles of data engineer and data scientist are closely related. Indeed, both will often work closely together within a team towards the same common goal with some overlap in data processing duties. Each role, however, has its own set of core responsibilities to handle when it comes to data.

    While a data engineer is primarily focused on infrastructures and systems we've defined above — a data scientist uses these data resources to generate analysis, insights, and predictions to assist teams in moving toward their goals. In effect, a data scientist uses data and tools provided by engineers to build models and extract information that would have been otherwise impossible to find.

    Working in tandem, both fields complement each other to create a cohesive team capable of delivering a competitive advantage in business.

    Benefits of Data Engineering Services to Businesses

    Big data is increasingly vital for organizations aiming to improve their decision-making, extend their services, or improve their business intelligence. The ability to take full advantage of data lies securely in the ability and productivity of your data engineering teams.


    Some of the benefits that our data teams regularly provide to partner organizations include:

    • Improved internal decision-making. By building vital systems and processes for organizations we've been able to allow teams to extract insights and value from their data to make decisions with firm logical reasoning and a solid grounding in good data.
    • Better efficiency. Having an expert team on hand allows many of the necessary steps of data collection and processing to be automated.
    • Strong data governance. With an abundance of experience and knowledge in all things data we ensure security, accuracy, and regulatory compliance are key priorities in data handling and processing.
    • Scalable systems. Big data is designed to be inherently scalable. Reaching true scalability, however, requires storage solutions, pipelines, and services configured well and built to last — something we specialize in for our partner businesses.
    • Cost savings. Data services can implement effective storage and processing solutions that maximize every unit of storage and processing purchased from providers.

    The most impactful benefits of effective data engineering services come from deploying a cohesive reliable solution aimed at addressing your organization's specific needs and requirements.

    Our Role as a Data Services Provider

    The decision to work with a data engineering team to revolutionize your workflows can be both one of the most daunting and most impactful decisions you can make. Our teams combine data scientists, data engineers, and data analysts alongside project managers and technical support staff to provide the domain knowledge, technical expertise, and industry experience necessary to succeed in the field.

    Combining diverse skill sets and expertise in this way enables us to build with a focus on complete and comprehensive services starting from the data gathering infrastructure and ending in reporting, data visualization, and predictive modeling.

    Invariably, this cross-discipline expertise and knowledge translates into providing a high-value investment for organizations aiming to leverage new and existing data sources to improve future services.

    Managing Big Data

    In today's technology environment data really is as valuable as oil. Much like oil, however, accessing big data to process and extract that value is no easy task. When broken down to its simplest form, data engineering is the process of finding, mining, and processing big data to realize its value.

    Our data teams build systems that gather, store, process, and utilize your data — turning it into better decisions, decisive action, and better real-world results.

    Finding out how to leverage your unique data sets to generate growth in your business is the first step toward reaching those results. Simply get in touch with our data engineering teams to discover the insights held within your own data and how it can be used to transform your organization.

    Jovica Turcinovic Partner & Salesforce Lead

    Coach. Divergent thinker. Passionate about digitalisation. Firmly believes that all starts and ends with good interpersonal relationships.