As businesses increasingly rely on metrics to drive decision-making, the underlying data architecture becomes a critical determinant of success. In his role as VP of solutions engineering at high-performance open source distributed SQL database company Yugabyte, Amey Banarse is well-versed in knowing the difference between an ‘okay’ information substrate and a scalable & resilient data architecture that is capable of performing within demanding (possibly real-time, often highly-distributed) modern enterprise application environments. What then are the factors IT directors need to identify in order to know whether their data backbone is fit for purpose?
In the pursuit of a precision-engineered data architecture that will enable supporting systems to grow with an organisation, withstand disruption and adapt to emerging technological trends, there are many factors to consider… but we will focus on just three here today: resilience attributes, SQL Compatibility and support for multiple data models.
Pillar #1: Data distribution equals resilience
Banarse suggests that data distribution is the cornerstone of a resilient architecture. It involves dividing data into manageable segments (sharding), duplicating data across multiple nodes (replication) and ensuring continuous availability even in the face of failures.
Banarse suggests that data distribution is the cornerstone of a resilient architecture. It involves dividing data into manageable segments (sharding), duplicating data across multiple nodes (replication) and ensuring continuous availability even in the face of failures.
“First let’s think about sharding,” said Banarse. “By partitioning databases into smaller, more manageable pieces, sharding enhances performance and allows systems to handle large-scale data more efficiently. Effective sharding strategies distribute data evenly, preventing bottlenecks and enabling horizontal scaling. Secondly, think about replication i.e. data replication involves maintaining copies of data across different nodes or geographical locations. This redundancy ensures data availability and reliability, minimising the risk of data loss due to hardware failures, network issues, or other disruptions.”
The third element of data distribution excellence depends on what kind of approach an organisation has to availability. High availability is achieved through a combination of sharding and replication. Implementing robust failover mechanisms and load balancing ensures that data services remain operational, providing uninterrupted access to users and applications.
Asked to list his best practices for data engineers approaching these concerns today, Banarse details this triumvirate of alert zones:
- Implement automated sharding and replication strategies to minimise manual intervention and reduce the risk of human error.
- Use multi-region deployments to enhance data availability and disaster recovery capabilities.
- Monitor data distribution metrics continuously to proactively identify and address potential performance issues.
Pillar #2: SQL compatibility
The Yugabyte team are understandably bullish about Structured Query Language (SQL) as a fundamental tool for data manipulation and analysis. It’s in the company’s DNA, it only stands to reason. That being said, SQL ensures integration with both established and emerging data engineering tools and that includes compatibility with popular open – source projects like PostgreSQL, all of which enables software engineers and data scientists to use a widely adopted SQL foundation for their applications.
“The rise of cloud-based data warehouses and analytics platforms, like Snowflake and BigQuery, underscores the enduring relevance of SQL,” said Banarse. “These platforms offer advanced SQL capabilities while integrating with machine learning tools, real-time data processing systems and visualisation software. They enable organisations to derive deeper insights and drive innovation. SQL’s widespread adoption means that organisations can leverage a vast array of existing tools and expertise. This reduces the technical learning curve for teams and accelerates the deployment of data solutions.”
As the data engineering landscape continues to evolve, Banarse and team think that new tools and technologies will emerge that encompass entire end-to-end data architectures.
These new tools will encompass everything from real-time data streaming ingestion to transactional databases for operational workloads; they will also span from analytical systems for OLAP processing to the serving layers that deliver actionable insights. The suggestion here is that a data architecture that maintains SQL compatibility can easily integrate with modern data pipelines, analytics platforms and machine learning frameworks to ensure longevity and adaptability.
Banarse again details best practices for data engineers in this area:
- Adopt data platforms that offer robust SQL support and compatibility with other query languages and APIs.
- Invest in training and development to ensure that teams can effectively utilise SQL within the broader data engineering ecosystem.
- Regularly evaluate and integrate new tools that enhance SQL’s capabilities and extend its reach within the organisation’s data workflows.
Pillar #3: Support for multiple data models
Modern data engineers must consider a variety of data models to accommodate diverse use cases and prevent data platform sprawl. By embracing multi-modal data models, data engineers benefit from the flexibility and agility they need to innovate and respond to evolving business needs.
“Supporting multiple data models (e.g. relational, document, graph, key-value) affords them the flexibility to choose the most appropriate model for each use case, optimising performance and efficiency. It also affords the agility to experiment with different data structures and paradigms, fostering innovation and enabling rapid adaptation to changing requirements,” said Banarse. “By integrating various data models within a single platform, modern data engineers can reduce the complexity and overhead associated with managing disparate systems. This allows them to streamline data management processes and enhance operational efficiency.”
Best practices offered by Banarse and team here are (aside from his suggestion to select data platforms that natively support multiple data models) follows:
- Encourage a culture of experimentation and learning, allowing developers to explore and utilise different data models as needed.
- Implement governance and standardisation practices to maintain consistency and integrity across diverse data models.
Key takeaways
We may infer from this discussion then that modern data engineering era is all about scalable and resilient data architectures. Robust data distribution, compatibility and support for the widest range of data types, structures, platforms, and indeed models.
“These three pillars not only ensure that data systems can handle current demands but also provide the flexibility and resilience organizations need to adapt to future challenges and opportunities. By prioritising these foundational elements, modern data engineers can create data architectures that drive sustained growth, foster innovation, and maintain a competitive edge in an ever-evolving digital landscape,” concluded Banarse.
In an era where data is a critical asset, investing in scalable and resilient data architectures is a strategic imperative. These pillars may help organisations harness the full potential of their data.