Data is the lifeblood of the modern organization. It is constantly moving and changing, vast in scale and dizzying in velocity. Distributed across various platforms, data drives operations and informs strategic decisions. To manage this complex landscape, more organizations are implementing an “operational data store” (ODS). An ODS serves as an intermediary between a wide array of data sources and data consumers, including business analysts, developers, and senior decision-makers. This article delves into the process of creating an operational data store using TiDB, an open-source distributed SQL solution, highlighting its advantages and key considerations.
The Role of an Operational Data Store
An operational data store differs significantly from a data warehouse. While a data warehouse is a repository where data is shaped and organized for business intelligence and historical analysis, an ODS acts as a temporary landing zone where data from various organizational sources is consolidated and transformed for real-time use. The ODS is essential for understanding the current state of the business, enabling real-time decision-making and supporting data governance, privacy, and compliance.
Operational data stores synthesize data from sources such as CRM systems, IT ticketing, HR, marketing, customer service, and other functions. Common use cases include:
Supporting data-driven decision-making in real-time: By providing up-to-date information, an ODS helps organizations make informed decisions quickly.
Improving data governance, privacy, and compliance: An ODS can enforce data policies and ensure compliance with regulations.
Modernizing legacy systems through data-as-a-service (DaaS): By providing a unified data access layer, an ODS can extend the life of legacy systems.
Efficient data processing: An ODS enables efficient data integration and processing, facilitating smoother operations.
To implement an operational data store effectively, a robust technological infrastructure is required. TiDB, with its high-performance capabilities, is well-suited for this task.
Technological Requirements for an Operational Data Store
When selecting a data solution for an ODS, consider the following key requirements:
Scalability: The ODS must handle large volumes of data from multiple systems and support real-time queries. TiDB’s distributed architecture allows it to scale horizontally, accommodating growing data demands.
Performance: Real-time responses are crucial for ODS users. TiDB’s hybrid transactional and analytical processing (HTAP) capabilities ensure low-latency query performance.
Reliability: An ODS needs to maintain operations under intense loads and isolate system failures. TiDB’s high availability and fault tolerance features ensure continuous operation.
Flexible queries: The ODS should support a range of use cases, from business intelligence to real-time data processing. TiDB’s SQL compatibility and support for complex queries provide the necessary flexibility.
Implementing an Operational Data Store with TiDB
Once TiDB is selected as the data solution, several key areas need to be addressed during implementation:
Capacity planning is essential to ensure that the ODS can handle the expected workload. TiDB separates storage from query processing, requiring independent sizing of the SQL and storage layers. Consider the following factors:
Storage Needs: Determine the overall data volume and account for query workloads, as some queries will be processed at the storage layer. Start with an estimate, such as 2TB to 4TB of storage and 16 cores per node, and adjust based on real-world workload testing.
Throughput: Focus on the queries per second (QPS) metric for SQL nodes. Benchmarking with real-world data can help determine the capacity of each node. TiDB’s flexibility in adding and removing compute resources facilitates this process.
Schema design is critical for optimizing performance and scalability. If migrating from single-node relational databases like PostgreSQL or SQL Server, maintain the existing schema but consider adding or modifying indexes. For greenfield implementations, take advantage of TiDB’s support for online schema changes, allowing you to adapt the schema as data volume grows or new requirements emerge.
Adopting TiDB enhances an organization’s ability to leverage real-time data across various operations. Effective integration involves:
Data Ingestion: TiDB supports multiple data ingestion protocols and connectors for popular databases like MySQL and Apache Kafka. This compatibility ensures smooth integration with existing infrastructures.
Data Synchronization: Use TiDB’s Change Data Capture (CDC) feature to stream changes in real-time, maintaining data consistency with external data stores like data lakes and warehouses. TiDB Lightning facilitates large-volume data transfers.
Application and BI Tool Integration: TiDB’s SQL interface allows seamless integration with BI tools like Tableau, Power BI, and Looker. Compatibility with compute engines like Apache Spark and Flink enables complex data processing workflows.
An operational data store is typically shared among several services with different requirements and priorities. TiDB’s Resource Control feature provides fine-grained resource management, ensuring optimal performance and cost-effectiveness. This feature allows for precise allocation of compute and storage resources, enhancing user experience and operational efficiency.
Making the Most of Data with TiDB
In today’s fast-paced business environment, data plays a crucial role in driving decisions and improving efficiency. An operational data store built with TiDB can help organizations achieve seamless connectivity between various data sources, data warehouses, data lakes, and end-user applications. TiDB’s robust data ingestion, synchronization, and compatibility with BI tools, along with its ability to handle large-scale data operations with minimal latency, make it an excellent choice for this purpose.
For example, a financial services company might use TiDB to consolidate data from CRM systems, transaction records, and customer support logs. By processing this data in real-time, the company can provide personalized services, detect fraud quickly, and ensure compliance with regulatory requirements.
Another example could be a healthcare provider using TiDB to integrate patient records, treatment histories, and operational data. This integration enables real-time monitoring of patient care, streamlined administrative processes, and improved decision-making based on comprehensive data insights.
Conclusion
Building an operational data store with TiDB enables organizations to harness real-time data for strategic decision-making and operational efficiency. TiDB’s scalability, performance, reliability, and flexible querying capabilities make it a robust solution for modern data management needs. By addressing key considerations such as capacity planning, schema design, ecosystem integration, and resource management, organizations can create an efficient and effective operational data store.
In the era of innovation and global competition, data is more than just a resource—it is a strategic asset. An operational data store like the one described here helps businesses adapt to the new reality, where data-driven decisions are essential for success. Implementing TiDB not only enhances data processing capabilities but also positions organizations to thrive in a data-centric world.
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