Your company's database architecture is getting more complex. How will you manage the growth?
As your company's database architecture becomes more complex, you'll need to adopt strategies to manage this growth. Consider these approaches:
How do you manage your company's growing database architecture? Share your strategies.
Your company's database architecture is getting more complex. How will you manage the growth?
As your company's database architecture becomes more complex, you'll need to adopt strategies to manage this growth. Consider these approaches:
How do you manage your company's growing database architecture? Share your strategies.
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First thing first, go to drawing board and re-look/re-architect the data model designed at the start of the product development. All other parts like scalability, partitioning, sharding etc. come later. In my experience, I have often seen people jumping to the tech/infra solutions before looking into this fundamental aspect of database!
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Managing a growing and increasingly complex database architecture requires thoughtful strategies. Mentioning a few basic and important ones here- 1. Periodically review and optimize the data model to avoid redundancy and inefficiency. 2. Regularly archive/delete old or infrequently accessed data 3. Ensure the team stays updated with evolving database technologies. 4.Test migrations thoroughly in staging environments before deploying to production. 5.Optimize joins and subqueries by leveraging features like Common Table Expressions (CTEs). 6.Build a separate data warehouse or data lake (e.g., Snowflake, Amazon Redshift, BigQuery) for analytics to reduce the load on the operational database.
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Managing a growing and increasingly complex database architecture requires proactive management. For example: 1. Before every application changes, review and optimize the data model 2. Gather statistics of indexes and evaluate their performance and usages 3. Plan to archive old data 4. Optimize queries 5. Build another database for reporting and prevent huge reporting workloads 6. Ensure the team stays updated with evolving database technologies. 7. Monitor session activity and distinguish bad plan queries and change them if possible
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Designing a great database starts with understanding your data. Choose the right database engine for your needs—relational (MySQL/PostgreSQL), NoSQL (MongoDB/Cassandra), or specialised solutions like Redis for real-time or InfluxDB for time-series data. Organise smartly by distributing data across databases or partitions based on behaviour and application—think functional separation or sharding for scalability. Finally, tune and monitor regularly. Optimise queries, adjust configurations, and track metrics like latency and resource usage with tools like Grafana. A well-designed database powers seamless performance!
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I have reviewed some DB Architectures in oracle. In all cases, tables are created using default value of physical parameters like PCTFREE, PCTUSED etc. We can optimize these parameters as per business need. Likewise parallelism and cache options also for various queries and UDF. Some other common issues implicit datatype conversion because of datatype inconsistencies between variables, parameters and table columns. Some cases are either under indexing or over indexing, no efforts for index consolidations. Another big messup is repeating SQLs, unnecessary joining. Almost all applications we need to work with datetime datatypes, in this case also usage are not well thougts. Transactions are not well defined, commit is used after all DML.
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Based my experience, here is my suggestion. 1. Most of the time it grows due to horizontal expansion and poor design implementation 2. Ask a question why the new entities are added and why can't leverage the exisiting one. 3. Regular audit on design and solution 4. Adopt AI solution where most of the complex cases are taken care which reduces the expansion 5. Review the source data or new sources and evaluate how it adds value to business 6. Necessary clean up of entities in regular intervals 7. Incorporate AI driven BI solutions
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Managing the growth of a company's database architecture requires a strategic and scalable approach to ensure performance, reliability, and maintainability. With growing data volumes and increasing table complexity due to new business functionalities, I focus on implementing scalable architectures such as partitioning and replication to handle data loads efficiently. Transitioning from monolithic databases to a microservices-oriented design, where each service manages its own database, helps reduce complexity and improve scalability. By combining scalable design, proactive monitoring, automation, and continuous improvement, the database architecture can effectively adapt to growth without compromising performance or reliability
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Best way to keep database load free to develop middle tiers which can cache very frequently used data and big data should be divided into pieces and build so that at a single time single piece can satisfy business requirements. As pages are loaded into buffers consume much space which decreases performance. We used partitioning but that too is costly solution for businesses like banking which has very huge DML operations. Best middle tiers works like micro-services for databases.
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When the database architecture gets complex, I suggest the following steps: 1- review the business needs and rules. What is the main reasons responsible for this complexity? 2- discuss these business needs with all stakeholders, and check what still needed and what not. 3- according to the new business rules and needs, try to enhanse the database structure or build a new one, try to use some new database ( NoSql ) ones if it will enhanse the business. 4- dedicate people for the previous mentioned process, and for the deployment as it has to be fully compatible with the front end to keep the changes as minimal as possible. 5- Break down the whole process and prioritize each one to allocate them in sprints
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hello I will try to summarize the main. I typically follow these strategies: 1. Assessing Needs: Identify project requirements such as data volume, type, and security, ensuring the vendor meets these needs 2. Vendor’s Track Record: Evaluate the vendor’s reputation, technical support, and history of consistent updates 3. Scalability: Choose a solution that supports future growth to avoid costly migrations. 4. Security and Compliance: Focus on features like encryption and compliance with standards like GDPR 5. Cost vs. Value: Assess overall value, balancing features, support, and cost. 6. Testing: Simulate real-world scenarios to verify performance and capabilities Clear requirements and a thorough evaluation ensure an informed choice.
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