Is your business ready to handle the explosive growth in big data? According to a report, data access is a common obstacle for organizations. The report shows that companies are wasting around 6-10 hours per week managing data access. This issue is coupled with issues related to data security, which leads to missed business opportunities but also makes them feel powerless in the cloud environment.
Thus, without a scalable data engineering architecture, you risk inefficiencies and data inaccuracies, leading to reduced customer satisfaction and lost revenue. That's why scaling your data engineering architecture can be beneficial for you!
Here's everything you need to know to design a scalable architecture that delivers high performance and accuracy.
Importance of Scaling Your Data Engineering Architecture
Did you know that the global big data market size is expected to grow from USD 138.9 billion in 2020 to USD 229.4 billion by 2025?
Having a scalable data engineering architecture will ensure that your business is ready to handle any increase in data complexity or volume by delivering a framework that’ll easily adapt to new versions of the changing data landscape.
Scaling your data pipelines will award your business a competitive advantage since your business will be able to process data faster with better accuracy. Thus, it will be able to respond to transforming market conditions promptly and make better-informed decisions.
In addition, designing a scalable data engineering architecture diagram gives your business the foundation it needs for growth and innovation. It’ll allow your firm to experiment with new technologies, processing techniques, and data sources, ensuring opportunities for growth and new insights.
Understanding Your Data Engineering Architecture Before Scaling
Here are a few steps that can help you understand data engineering architecture:
- Analyze the Current Architecture
Start by going through your business’s current data engineering architecture. Do so until you completely comprehend all existing components and their interactions. Also, identify the distinct systems and data stores that make up the current architecture.
2. Note All Areas of Weakness and Bottlenecks
Note down all the bottlenecks and areas of weakness you’d like to tackle. This step of the process will give you a better understanding of the kinds of improvement required so the noted areas can handle increased volumes of data.
3. Review Data Sources and Their Processing
Analyze the data sources feeding into your architecture, and while at it, identify the different types of data as well as their frequency of arrival. Data & analytics should help you ensure you’ve designed a scalable architecture that will handle the data volume and frequency.
4. Document Your Current Data Engineering Architecture
Before scaling your data engineering architecture, document the current architecture. This ensures that every stakeholder has a clear comprehension of the existing architecture and areas that need improvements.
The documentation should include system interactions as well as data engineering architecture diagrams.
Plan the Scaling
Now it’s time to start planning for scalability by following these simple steps:
- Understand Your Future Requirements
The first step is to comprehend all your business’ future requirements. Analyze your firm’s expected data processing needs as well as growth projections.
2. Pick the Right Technology
Picking the right tech is arguably the most essential step when designing a scalable data engineering architecture. Carefully evaluate the technologies available in the marketplace and pick the one that best aligns with your business’s requirements and growth projections.
For instance, you might want to consider technologies like Apache Spark or Apache Hadoop to handle increased volumes of data.
3. Design for Horizontal Scaling
Designing for horizontal scaling implies that you’ll be able to add more nodes to your existing data engineering architecture whenever you want it to handle increased data volume. To do this, consider using message queues and data stores.
4. Monitor and Optimize Performance
When you are scaling data pipelines, testing is an important step. To make sure that the scaled architecture still performs exceptionally, monitor and optimize for performance constantly.
5. Plan for Disaster Recovery
Make disaster recovery plans to assure that your data engineering architecture will remain in operation even in an event of a disaster. The plan should include backup and recovery procedures as well as failover mechanisms.
Some Challenges of Scaling Data Engineering Architecture
Scaling data engineering architecture can be challenging for organizations. Here are some of the common challenges they face:
- Complexity
As the data engineering architecture evolves, it becomes increasingly complex. Organizations need to manage a large number of data sources and systems, making it challenging to maintain and troubleshoot the system.
2. Increased Cost
Scaling data engineering architecture requires investing in hardware, software, and skilled personnel. As the volume of data grows, so does the cost associated with processing and storing it.
3. Interoperability
Scaling data pipelines may require you to integrate new data sources, systems, and applications. This can lead to interoperability challenges, such as incompatible data formats or systems that cannot communicate with each other.
4. Data Governance
With the growing volume of data, data governance can become challenging, including data lineage and data ownership. You need to implement robust data governance practices to ensure data is managed effectively while scaling your architecture.
How to Scale Your Data Engineering Architecture
Scaling data engineering architecture can be a challenging task, but some strategies can help:
- Horizontal Scaling
Horizontal scaling is one of the easiest ways of scaling data engineering architecture. It involves adding more nodes or servers to your current data processing infrastructure.
You can achieve this by setting up distributed systems that can handle more data processing requests by spreading them across a multitude of servers or machines.
2. Vertical Scaling
This approach involves adding more computing resources to the same server or single machine. This can be achieved by adding more RAM, CPU, or storage to the existing server or computer so it can better handle additional requests.
3. Cloud-Based Scaling Solutions
Cloud-based solutions offer scalability and elasticity that can make it easier to scale data engineering architecture. With these solutions, you’ll only pay for the resources you use, making it a cost-effective solution.
4. Data Partitioning
This approach involves dividing your business’ data into smaller partitions and then processing each partition on a different server. Data partitioning helps reduce the load on each server and permits better parallel processing of existing data.
Companies Using Data Engineering Architecture
Netflix uses a data engineering architecture that is capable of handling massive amounts of data generated by its millions of users. They have built a data lake that integrates data from different sources and processes it using Apache Flink, Apache Spark, etc.
Amazon has a highly scalable and distributed data architecture that is capable of processing billions of transactions per day. They use a combination of Hadoop, Apache Spark, and other open-source big data tools to process and analyze data.
Facebook's architecture is capable of processing billions of user interactions per day. They use a combination of Hadoop, Apache Spark, and other open-source big data tools to process and analyze data from their social network.
According to a report, data-driven organizations are 23 times more likely to acquire customers and 19 times as likely to be profitable. Therefore, scaling your data engineering architecture is essential for businesses.
By following the best practices, you can better prepare for the future and ensure your systems remain resilient and scalable. It’s essential to keep in mind that scaling your data engineering architecture won't be a one-time event. It’s an ongoing process that’ll need continuous optimization, monitoring, and evaluation.
Refer our previous blog, How to Build a Modern Data Architecture for Business?