In this age, with the ever-increasing pace of technological innovation and competition, it has become critical for large businesses, and even more so for growing corporations, to utilize data to generate actionable insights and develop strategies that enable better business opportunities.
With increasing global spends of over $4241 billion on IT, the field is expected to emerge as a significant avenue of investment for businesses. Foundational to this growth is the processing, storage, and retrieval of informational data, given the increasing need for organizations to scale, go digital, and pivot to agile cloud methodologies.
Consider having access to a spreadsheet that contains every possible piece of data you have about your customers – what plans they are subscribed to, what their buying decisions for specific products are, what marketing campaigns have influenced their decision to sign up for your product/service, every action they’ve taken on your website or within an application. The insights to be gained from, and the applications of, this data are truly endless.
To achieve this kind of data collection and organization, SQL (Structured Query Language) databases have been in use for years, helping businesses store and retrieve data instantaneously, through simple queries. From customer data to inventory data and even HR operations data, everything can be stored in a single place using SQL databases.
Consider the example of a headphone manufacturing company. Every headphone receives a serial number, which helps track multiple sets of information, ranging from where the plastic of the packaging is procured to where the headphone will be shipped. In case your customer raises a complaint regarding a pair of headphones, these can be tracked using the same serial number as well. That’s not all – intelligent data retrieval and processes can give you the power to track headphone trends, anticipate orders for replacements, and understand areas of improvement for your product. These insights can further be drilled down to a granular level – for instance, if components from Supplier A indicate a 20% failure rate whereas those from Supplier B show a 5% failure rate, such patterns can be used to assess quality standards and contracts.
What’s more, these insights can be derived using SQL queries, through functions or stored procedures rather than complex programs. This way, businesses can get the most out of the SQL optimiser rather than creating another layer atop the database.
Moreover, SQL’s sole design, based on usability, extensibility, data integrity, and performance, helps manage databases and, most importantly, helps businesses ensure data protection. In fact, a great business can only be as successful as its IT operations allow it to be. If data retrieval is hindered by multiple factors, from poorly written queries to indexing issues, resulting bottlenecks can slow down performance and reduce productivity for the entire organization.
Scaling up databases would be one solution; however, this would lead to additional costs. Nevertheless, these can be managed through simple configurational changes and optimizing written queries (and only after the optimization of existing resources would scaling up be recommended).
Hence, SQL optimization becomes crucial for a business to reduce latency issues. For instance, long wait times for customers to get lists of their favourite restaurants and cuisines, in the context of a food delivery company, or similar delays for analysts generating reports, are a few of the challenges that can be mitigated through such optimization. The following are a few points to keep in mind, to determine whether your firm needs SQL optimization:
1. Can your database handle growth in users or application features?
SQL queries can work efficiently with minimum execution time, given a certain number of users. However, consider that you own an e-commerce platform, and you are sure that, with your current branding strategy, your number of platform users will increase. You are likely to see a surge in data transactions, i.e., the retrieval of product catalogs or information regarding recent orders. Amidst this, your database servers may not have the capacity to handle too many complex query transactions, leading to an overload. This in turn would lead to an increase in queries’ transaction time, and delays in application responses. Therefore, assessing the volume and growth of queries becomes important to ensure efficiency. Analyzing ELT/ETL pipelines, capturing data transmission, and logging the same would help plot trend charts on a tool such as Tableau/PowerBI, helping teams stay up to date with growth trends.
2. Are incidents on latency from end users increasing?
When there are multiple incidents raised regarding latency in applications, with users, for instance, flagging off slow loading times and displays for a website, or even churning due to slow response times, most management teams would be inclined toward immediate fixes by vertically scaling resources. While this might be effective in the short term, scaling up would lead to additional infrastructure and management costs, requiring a larger workforce to manage infrastructure. Instead, regular checks on optimization, when invested in, can go a long way, helping reduce application run times and lowering infrastructure costs. If the problem still persists, however, then it’s a good time to scale up.
3. How are key System Metrics performing?
There are many methods of monitoring “red flags” or problem areas in ecosystems, thus enabling timely resolution. The best place to start is with basic system metrics. Here, close attention should be paid to increasing latency in a company’s application, with latency indicators broken down to identify the issue. Given that the fundamental components for an enterprise data warehousing product such as SQL Server, Snowflake, or Oracle are the CPU, memory, and disk, evaluating the metrics of these components can drive businesses in the right direction. There are numerous tools available, with excellent UIs, to obtain statistics on these elements, and they should provide early warning signs, helping businesses be prepared with the right optimization at the right time.
4. What logs are long running Queries registering?
Every good Database Administrator (DBA) has a tool to monitor slow queries, whether through application metrics or a slow query log. If escalating response times are observed, tracing the underlying issue becomes important. Very often, this could be due to normal tables or volume growth, but it could also point to inefficiencies in SQL queries, which can be resolved by developers. Looking at optimization on the SQL query and/or looking at creating indexes or infrastructure changes would be the next step, if your logs are pointing towards a long-running SQL query.
Today, business enterprises are data driven. And this data must be stored, protected, and used efficiently – making a database management system such as SQL, with optimized versions, essential.
While investing in SQL optimization will ensure better ROI in the long term, here are a few recommendations to help businesses improve performance, reduce long-term resource consumption, and reduce costs and time in the short term: