8 Indexing Strategies to Optimize Database Performance

Databases provide the backbone for almost every application and system we rely on, acting like a digital filing system for storing and retrieving essential information. Whether it’s organizing customer data in a CRM or handling transactions in a banking system, an efficient database is crucial for a smooth user experience.

However, when we get into large volumes of data and more complex queries, database management can become daunting. That’s where good indexing strategies can make all the difference. 

Think of it like tidying up your digital filing cabinet so you can quickly find what you need without rummaging through multiple drawers and folders to locate the correct file.

By organizing and structuring your data in a way that facilitates quick retrieval, indexing can make a big difference in how well your database performs. Here, we’ll explore some strategies to help you do just that.

Database indexing best practices

Before you settle on a strategy, it’s worth understanding the different ways you can approach indexing to improve query selection and overall database performance.

Identify key queries and columns

Before getting started with indexing, you need to identify the type of queries your application is running regularly and which columns are involved in those queries. This helps you to focus your efforts on areas that will give the best results. There’s no point in spending time and energy indexing columns that rarely get used.

For example, let’s say you’re developing an app for an online bookstore, and one of the most common queries is searching for books by author name. In this case, creating an index on the “author” column can dramatically improve the performance of search queries.

Data orchestration tools can examine query patterns and usage statistics to pinpoint the most commonly executed queries in your database. What is orchestration, we hear you ask. 

When we talk about data, orchestration is the process of managing and coordinating various tasks like collecting, processing, and analyzing data from different sources. This helps to keep data operations well-organized and efficient.

By understanding which queries are commonly used, database administrators can prioritize indexing efforts on the columns involved in these queries.

Avoid over-indexing

While indexing can undoubtedly speed up query performance, as the saying goes, you can have too much of a good thing. 

Over-indexing isn’t just a waste of time, it can actually have the opposite desired effect and hinder database performance.    

Keep in mind that every index you add takes up storage space and needs managing within the database. Plus, having too many indexes in play can slow down your insert and update performance because your database will be working overtime to update multiple indexes with every change.

To avoid this, follow data indexing best practices such as those covered in Apache Hive documentation. Aim to strike a balance between query performance and keeping the database easy to manage. 

Focus on indexing columns that are frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses. Also, think about using composite indexes for queries that involve multiple columns.

Regularly monitor and tune indexes

Creating indexes isn’t one of those jobs you can do once and forget about. Because data and query patterns often evolve over time, you need to regularly check and adjust them. 

It’s similar to the practices of Machine Learning Ops (MLOps), where ongoing monitoring ensures the model is still effective. Similarly, consistently reviewing and fine-tuning indexes plays a pivotal role in managing their effectiveness. 

Failure to do so can lead to accumulating technical debt, where outdated or inefficient indexes accumulate over time, resulting in degraded performance and increased maintenance overhead.

Use SQL tools like MySQL’s EXPLAIN or Microsoft SQL Server’s Query Execution Plan. These will give you a solid view of how queries are being executed and which indexes are well utilized. You can then more easily see where to add missing indexes and remove ones you no longer need. It also helps you spot opportunities to update existing ones to better suit query patterns.

Let’s look at what that means in practice. Suppose you notice a particular query performing poorly despite having an index. Upon closer inspection, you discover that the index’s cardinality (i.e. uniqueness) is low, leading to poor selectivity. In this case, modifying the index or adding additional columns to improve selectivity could significantly boost that query’s performance.

Consider using covering indexes

A covering index includes all the columns necessary to fulfill a query. This means that the database doesn’t need to keep accessing the underlying table. 

To return to our filing cabinet analogy, you can think of it as having the right folders set out in front of you so you don’t have to search through the entire cabinet to find what you need. Using covering indexes can speed up search queries by reducing the number of overall disk I/O operations.

For example, consider a call center analytics software that logs details of each customer interaction. This might include data such as:

  • Caller ID
  • Call duration
  • Call timestamp
  • Outcome

If you’re frequently running reports on the total duration of calls, creating a covering index on the caller ID and call duration fields can optimize query performance. This allows the software to retrieve call duration information directly from the index without having to repeatedly access the main call log table.

Monitor and manage index fragmentation

Index fragmentation occurs when the logical sequence of index pages is not in sync with the physical arrangement. This can make data storage less efficient and slow down search queries. It’s like a library’s card catalog not matching the actual locations of the books on the shelves. 

If you don’t catch this and fix it, the problem will only get worse as more data is added or updated. It’s essential to keep a close eye on your indexes and tidy them up regularly. 

One solution is containerization, which provides a structured environment for managing databases. Most modern systems also offer tools for detecting and addressing index fragmentation like rebuilding or reorganizing indexes to help with this.

8 database indexing strategies to try

Not all indexing strategies are created equal. When it comes to finding the best indexing strategy for your database, you need to consider a few things, including:

  • What type of data you’re working with
  • Which queries you run often
  • What performance goals you want to achieve

With that in mind, here are a few examples of indexing strategies for different situations.

1. Single-column indexes

Single-column indexes work well for databases with tables containing a large number of rows and where queries frequently filter or sort data based on a single column. For instance, if you’re regularly looking up users by their usernames, create an index for the “username” column in the user table for faster retrieval.

2. Composite indexes

If your common queries involve columns in a WHERE clause or involve ORDER BY and GROUP BY operations on multiple columns, composite indexes might be more useful. For example, if you have a sales database where you’re frequently searching for sales by date and location together, you can create an index for both the “date” and “location” columns.

3. Unique indexes

These ensure data integrity by enforcing uniqueness on one or more columns. They are beneficial for columns that should not contain duplicate values, such as primary keys or email addresses in a user table.

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4. Clustered indexes

Some databases feature rows that are physically stored in order based on the index key. In these cases, clustered indexes can improve the performance of range queries or sequential scans. For example, if you organize time-series data by date, clustering the primary key will make it quicker to find information chronologically.

5. Covering indexes

These indexes contain all necessary information for answering a query so the database doesn’t have to revert to the original data table. They’re helpful for queries with SELECT, JOIN, and WHERE clauses. 

This can significantly improve query performance, especially in scenarios where you might need to generate data-driven insights from complex queries that involve multiple columns or tables. For example, if you often create reports using data from multiple columns, a covering index could include all those columns to speed up the process.

For organizations managing large-scale data processing tasks, such as those involving HPC batch jobs, implementing covering indexes can significantly improve query performance, especially when dealing with complex queries across multiple columns or tables.

Another crucial consideration for database optimization is ensuring smooth operations during critical periods, such as website launches. Utilizing a comprehensive website launch checklist can help ensure that your database infrastructure is adequately prepared to handle increased traffic and demands on query performance during such events.

6. Partial indexes

When a subset of data is frequently queried, partial indexes can be created to cover only that subset, reducing the index size and improving query performance. An example is creating a partial index for active users in a user table where only rows with “active = true” are indexed. 

In cloud environments dealing with massive datasets, partial indexes can help you manage resources more efficiently and maintain optimal performance. What is cloud native architecture? This refers to apps built specifically to work well in cloud environments. It involves using cloud services and concepts like microservices, containerization, and orchestration. It’s frequently used for apps that need to perform in an agile environment and be quickly scaled up or down.

7. Expression indexes

These indexes are created based on expressions or functions applied to one or more columns. They are useful for queries involving computed values or transformations. For example, indexing the result of a mathematical operation or string concatenation performed on columns.

8. Hash indexes

Particularly useful for equality comparisons, hash indexes can provide fast access to data with low cardinality columns or when accessing a large number of rows randomly. They are suitable for scenarios like indexing boolean or enumerated columns.

Database indexing – optimize database performance

In database management, optimizing queries is key to ensuring your database performs well across all platforms, from web to mobile. To do this, you need a solid indexing strategy. 

Choosing the right database index can directly impact business operations. When your database is well-organized, it means employees and users can find what they need quickly, leading to tangible benefits from improved response times to streamlined operations and reduced costs.

Understanding the different approaches and best practices means you’ll be better equipped to streamline your data and manage it efficiently.

Pohan Lin – Senior Web Marketing and Localizations Manager

Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing. Pohan has written for other domains such as Spiceworks and Parcel Monitor. Here is his LinkedIn.

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12 Ways to Optimize SQL Queries in Database Management

When it comes to data, the more you can gather, the better insights you can have. But this can lead to slow searches, so how do we find a middle ground?

With so many companies using the cloud to store large amounts of data, SQL optimization has become more important than ever. SQL (structured language query) is a programming language used to query and communicate with a database to extrapolate information. 

Do you want to speed up internal intel gathering, or ensure your customers don’t get bored and bounce? Let’s take a look at why you should be optimizing your SQL queries for better database management.

Why Optimize SQL Queries?

Imagine a customer searching for a product online and the results take a few minutes to appear. Would the customer wait? Probably not. For this reason, database managers must ensure SQL queries are optimized regularly for maximum efficiency. If a customer can’t find what they’re looking for within a reasonable time, they will go elsewhere.

It’s also crucial that developers optimize databases for mobile phone use since more and more people are using smartphones to shop.

But it’s not only customers who benefit from optimized queries. Slow search results can be frustrating for employees too as it leaves them unable to do their job to the best of their ability. This can be incredibly demotivating and even cause resentment in the workplace.

Meanwhile, quicker response times improve resource consumption, meaning more queries can be handled at once, improving the experience for both customers and staff.

12 Ways to Optimize SQL Queries

To get the most out of your SQL queries, there are several things you can do. Here we’ll look at some ways you can improve efficiency and make the end-user experience a more positive one.

1. Use indexes effectively

Indexes are special look-up tables used by a database search engine, sort of like how a reader would use the index in the back of a book. They can help speed up SQL queries as data that fits specific criteria can be located quickly. 

Indexes store data in one or more columns of a table, which means values can be identified easily.

Let’s say you work in customer support and gather data through your inbound call center technology. You could make a customer_ticket index, which would prevent you from having to scan the entire table to refer back to a prevent point of communication. Instead, you can simply look for a ticket number match condition to locate it.

Indexing frequent search criteria can ensure the best return speed, helping call center operatives provide the best service possible. However, too many indexes can slow down the database, so it’s best to focus on frequently used queries to index as this will avoid any slowing of data modification operations.

2. Avoid SELECT queries

SELECT queries are inefficient. This is because they view all the fields in a dataset rather than just the relevant ones. Instead, focus on retrieving necessary columns only.  By only selecting the fields that you need to view, models and reports will be clean and easier to use.

SELECT queries are often used as supply chain optimization techniques to determine supply chain issues such as calculating stock levels (see below).

3. Reduce the use of wildcard characters

Wildcard characters (like %) are used with the LIKE clause to substitute either a single character or a string of characters. Imagine you handle deliveries for a UK company, and need to find everything being sent to a certain location. UK postcodes are strings of 5 or six characters, with the last three narrowing down to a specific street. You could therefore search for something like:

SELECT * FROM Customers

WHERE CustomerPostcode LIKE ‘SE1%’;

This would show you every order in the SE1 area.

Wildcard characters can slow down query results because again, the database has to scan the entire table (known as a table scan) to find results. This is a slow and inefficient type of scan. It’s particularly challenging if you use them at the start of a search statement, because you’re instructing the database to find data where anything can precede your search query.

4. Use appropriate data types and layouts

Making sure you use the correct data type for each column can improve the query return rate. For example, use the DATE data type to store order placement dates instead of a general character field. This will reduce space as characters will be limited and return a faster query.

Using the correct data type can also protect against data entry errors which can help to improve the quality of the data. For example, a time or monetary amount couldn’t be entered in a date field.

It’s also worth considering the layout of your table – are the columns and rows in the optimal layout, or could you use SQL pivot row to columns in order to rearrange it into a better one?

5. Avoid redundant or unnecessary data retrieval

Whilst reducing SELECT queries focuses on columns, it’s also important to limit the number of rows you are returning in a query. This is because as the number of rows increases, the search speeds slow down.  You can do this by using LIMIT and restricting the data return to say, 100 or 200. This feature prevents the query from returning thousands of rows of data when you only need to use a few.

6. Use EXIST() instead of COUNT() queries

When searching for a specific element in a table, it’s more efficient to use an EXIST() keyword instead of a COUNT() one. This is because a COUNT query counts every instance of the specific search element – which can be very inefficient, especially if the database is large!

EXIST queries only count the first occurrence of the particular search element, which reduces search times and provides a more optimized experience.

7. Avoid subqueries

When subqueries are used in WHERE or HAVING clauses, they can slow down the performance of the query. This is because they can return large numbers of rows, making them difficult to execute.

JOIN clauses are often a better choice. The image below shows an example of both a subquery and a JOIN clause.

As you can see, the subquery at the top collects all of the customers’ IDs in the USA, and the outer query collects all the orders for the selected customers’ IDs. 

The JOIN query beneath returns the same result in a more efficient way by joining the two tables (CUSTOMERS and ORDERS) and selecting the orders where the customers are from the USA.

Both queries will work, but the JOIN query will be much quicker.

8. Make use of cloud database-specific features

Many cloud-based databases come with built-in features to optimize SQL queries. The automation in cloud-native databases in particular can make optimization much simpler. Not only can queries be optimized, but built-in features can also improve data security, access, scalability, and resilience. 

9. Monitor query performance

Checking on the run-time of your queries is key to identifying your poor performance queries. This allows you to optimize them, improve efficiency, and reduce costs.

Query profiling is one way of monitoring the performance of your queries. This involves analyzing statistics such as run time and amount of rows returned, looking at server speeds, database logs, and external factors to identify problem areas.

10. Utilize AI

AI can automate query optimization using solutions like rules-based AI. What is rules based AI? It’s a model that uses prewritten rules to solve problems and make decisions based on expert human knowledge. Alternatively, you can use machine learning algorithms. This is where AI ‘learns’ over time, meaning it can analyze query patterns and detect areas for automatic optimization.

This can save time (and money) by reducing the need for manual analysis.

11. Utilize microservice design patterns

Microservice design patterns can ensure large databases are broken into smaller databases (microservices) that serve different purposes. This is particularly useful to large corporations handling a lot of data. These can help to avoid the following problems:

  • Duplicated data
  • Data inconsistency
  • Different services/parts of the business needing to store data in different ways
  • Querying data

By utilizing microservice architecture, companies can build optimized, ready-to-query databases from the ground up. However, it’s important to have strong data governance policies in place in order to prevent data silos forming.

12. Use software designed to process large workloads

Software such as Apache Spark 3 using NVIDIA RAPIDS can provide adaptive query execution suited to the specific data that needs searching. This can lead to massive improvements in query performance as well as being more user-friendly, and lead to a better use of resources.

Final thoughts

There’s no doubt that cloud databases are a powerful tool for managing data. But, to get the most out of your data, database managers must ensure consistent optimization to make sure top performance. Simple steps such as indexing and swapping functions can be a step in the right direction.

Understanding and monitoring performance plays a key role in optimizing SQL queries and combined with simple steps such as indexing and swapping functions you can quickly make a difference to search time.

With these steps in mind, can you improve the performance of your database?

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2024 Guide to Collecting Crowdsourced Testing Feedback for Software Development

Did you know that the hole in the ozone layer above Antarctica went undetected for a long period of time? This was because all of NASA’s data analysis software was designed to ignore extreme measurements.

Feedback for Software development

Bugs in software can be frustrating for users, but they can also be catastrophic. Think about industries such as healthcare, banking, or national defense where a small error could have a devastating impact. 

Testing software thoroughly before it’s released is, therefore an essential part of the development process. But it’s not cheap. Startups, in particular, have to balance their finances, and getting the most bang for your buck when testing software is a huge consideration.

Tracking bugs and errors is one of the main challenges for any business about to launch a product. Here we’ll look at testing options and how to collect feedback from crowdsourced testers.

Software testing options

There are four main types of software testing options. Selecting the right one for your business can depend on your resources, namely time and money, as well as on your expected outcomes and any industry regulations. 

The four main options businesses have for software testing are:

  1. Using an in-house software testing team
  2. Outsourcing software testing
  3. Automated testing
  4. Crowdsourced testing

In-house testing teams can be expensive. This is because you have to recruit the right people for the job. You’ll also need to set up a testing environment that can handle testing for all operating systems on various devices. Outsourcing can overcome some of these issues, but it can also be costly as you’ll usually be billed at an hourly rate, whether bugs are discovered or not. 

This leaves crowdsourced testing, where a diverse group of testers use the product and report issues. 

What is crowdsourced testing?

Crowdsourced testing involves using the expertise of a range of people across the world (the “crowd”) to use and assess products. Because of the diverse nature of the group, this type of testing can offer a broad range of perspectives.

Software development

Crowd testers are usually found online and are given tasks or asked to do research to spot bugs or UX problems in the software. The testers are usually carefully selected to match your demographic, giving a much-needed ‘human’ element to the feedback generated.

Say, for example, your business is building a new website for the Indian Ocean region, and you register io domain. Crowdsourced testers would be given access to this domain and undertake a series of functions and tests to make sure the website works as it should. Because the testers are based in different geographical regions and use different devices, the test will be extremely comprehensive as some users may experience problems when others don’t.

This can give much quicker results than traditional testing methods.

What are the benefits of crowd-testing?

Whilst thorough testing in any form is a non-negotiable, crowdtesting has many benefits when compared to other forms of testing. Some of the reasons to consider crowdsourced testing are: 

  • It reduces testing costs
  • It’s more flexible
  • It covers a wider range of testing environments
  • It simplifies updates and innovation
  • It helps ensure the product meets customers’ needs and expectations
  • It allows for quicker identification and correction of bugs
  • It allows for quicker product launch times
  • It’s more scalable.

What to consider when collecting crowdsourced testing feedback

Whilst crowdsourced testing has its advantages, there are many things to consider to avoid potential problems occurring in the feedback process. These include:

1. Quality control and consistency

When faced with an array of diverse testers with differing levels of expertise, maintaining consistency can be a challenge. This is why you’ll need to issue clear and strict guidelines to testers to ensure good results.

Profiling testers can also help. A great crowdsourced tester should have the following qualities:

  • A wealth of technical skills and a lot of motivation
  • Good technological knowledge
  • Good communication skills
  • The ability to work as part of a team
  • The willingness and ability to follow the guidance.

2. Security and data privacy

Since the software is exposed to external testers, data privacy and security must be considered. Putting measures in place to protect and safeguard sensitive information and ensuring legal compliance is a must.

Clean room solutions can offer secure cloud-based communication, which could be a great option for liaising with crowdsourced testers. Knowing the clean room data recovery cost can help you factor secure communications into your testing planning.

2024 Guide to Collecting Crowdsourced Testing Feedback for Software development

3. Communication challenges

Because the ‘crowd’ is from a range of backgrounds, speaks different languages, and comes from different cultures, collecting feedback will require effective tools and strategies to standardize communication. 

Consider how testers can reach out with questions too. Cloud PBX systems allow businesses to scale up or down their communications in a lower cost way. The omnichannel aspect means that bug reporting or feedback can be streamlined via a channel that works for both the testers and your business.

4. Test case relevance and completeness

Test cases must cover all critical aspects of the software in line with testing objectives. This means careful planning, and developers must oversee this process with due diligence and care. 

Using a bayesian neutral network can help spot missing data using probable inference to estimate the missing value. Understanding what is bayesian neural network is, is a great tool for developers to create a testing environment that provides the best feedback.

5. Bug triage

It’s realistic to expect a high volume of bugs to come through. This can be overwhelming if the correct triage systems aren’t in place. The best way to triage bugs is by having a system to categorize and rank them in order of severity, UX, or impact so fixing them can be prioritized.

6. Tester reliability and motivation

Testers are individual people based in remote locations. Their level of care and due diligence can vary, and this can be a concern when collecting feedback. To ensure testers don’t rush the testing process, developers must consider how tests and feedback can be standardized. This could be done with verification checks, clear questioning, and shorter tasks.

Machine learning algorithms can spot patterns and make decisions based on findings. This can help to spot incorrect answers, so knowing how to evaluate machine learning models can help reduce feedback problems like this.

2024 Guide to Collecting Crowdsourced Testing Feedback for Software development

7. Contextual understanding

Because the ‘crowd’ is generally from a wide range of places, language barriers can be a problem, leading to different levels of understanding of a given brief, the product, or the intended audience. 

Developers must provide clear messaging, perhaps with easy-to-follow videos or diagrams, to make sure people understand the brief fully and provide the best quality feedback. It may also be worth considering pre-screening the testers to farm out those who may be unable to understand any aspects of the brief.

8. Response time and bug-fix turnaround

As they say, time is money, so the importance of a quick, well-coordinated response is important. When the ‘crowd’ is a global group, this can be a challenge to orchestrate, so proper systems must be in place. Having a response team working through the triage list can help.

A quicker turnaround can lead to a quicker, cleaner product launch that provides a basis for strong lead enrichment from the get-go. This is because using real people to test software helps build a product that customers will want to use and will therefore help create a stronger bond with leads.

9. Tester bias

When you work with a large group of people, opinions and bias can be a problem if not pre-considered. Biased opinions can affect feedback, so measures will need to be taken to identify this in the feedback process. Factoring this into your feedback model and addressing this in any guidance material can also help.

10. Compliance and legislation

Compliance and legislation in your region (as well as in the regions where your testers are located) must be taken into account when planning crowdsourced testing. This is especially true if sensitive data is shared or stored at any part of the process, from granting software access to feedback collation.

This is where your business’s policies and quality assurance will be paramount to a smooth testing process. Virtual machines can replicate a computer system, so are great for software testing as separate environments can be created away from the main system, which can help to keep real data secure and compliant.

2024 Guide to Collecting Crowdsourced Testing Feedback for Software development

Final thoughts

Deciding whether or not to opt for crowdsourced testing can depend on several factors such as the size, scope, and nature of the project. As a business, you’ll need to have an understanding of the complexities of the testing required, your time frames, and your financial resources to make the best choice.

Whilst all types of testing have their place, crowdsourced testing is a great option for companies wanting to thoroughly test their product whilst keeping costs to a minimum. Effective planning of the testing process can help overcome the challenges this type of testing can pose and provide the best-quality feedback.

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Six DevOps Trends to Learn About to Stay Ahead in the New Year

DevOps methodology is an ever-evolving field that supports successful digital transformation. Advances in tech, industry trends, and greater demand to meet customer expectations have led to a growing need for this kind of solution. There’s thus been huge market growth over the last few years, and this trend is predicted to continue into 2024 and beyond.

In fact, a recent study predicted the DevOps market will grow to $51.18 billion dollars by 2030—that’s up from $7.01 billion in 2021.

While the DevOps market can be unpredictable and is ever-evolving, there are some trends you need to know about to stay ahead as we move into the new year. In this article, we’ll touch on automation and AI, serverless architecture, and the importance of diversity and inclusion. 

Keep reading to ensure you’re ahead of the DevOps game as we enter 2024.

1. Automation and AI

When it comes to trends and technological advances across industries, there’s one thing that can’t be denied – automation and AI tech are here to stay. As the DevOps market evolves, the two will continue to play a key role in helping teams run more efficiently and analyze data more effectively. 

For example, automation tools can assist DevOps teams with handling data and delta streams. What are delta streams, you ask? Essentially, these simplify the act of streaming data into a lakehouse. 

As well as automation tools, the effective use of AI can lead to better decision-making and increased performance. 

Let’s first look at automation in more detail, before exploring the use of AI in DevOps further.


There are many benefits to automation, including:

  • Increased efficiency. Automated AI tools can often complete tasks quicker and more effectively than humans. For DevOps teams, this means increased efficiency and meeting goals and targets faster.
  • A reduction in man-made errors. Automation tools don’t tire in the same way employees do, and they aren’t affected by personal problems, lack of sleep, or the common cold. Automation reduces the risk of man-made errors by removing the human element of repetitive or monotonous tasks. 
  • Programming repetitive tasks. Automated tools can help DevOps teams program repetitive activities and therefore achieve their objectives faster. Doing so means staff can focus their energy on those tasks that aren’t yet able to be completed by digital technologies. 
brain tech

Artificial intelligence

Artificial intelligence can also be used in DevOps in a multitude of ways. For a start, predictive analytics can forecast future outcomes. By analyzing past deployments and performance metrics, AI tools can help teams improve their output.

Another trend in DevOps is the use of AI for operations and incident management. Using this effectively, teams can analyze data to detect and remediate issues faster. This can help predict problems before they occur and can be particularly useful for teams working on game development pipelines, for example. 

Ultimately, automation and AI in DevOps is a trend you need to stay ahead of. Delivering improved performance, increased efficiency, and the ability to predict and prevent problems ahead of time, neither one is going anywhere fast. 

2. Cloud-native technologies and serverless architecture

Cloud-native technologies allow organizations to run their operations efficiently by enabling them to build and utilize applications more effectively. For this reason, cloud-based technologies will continue to be widely adopted in DevOps as we head into 2024.

There are many ways in which DevOps teams can use these, including cloud data management and migration. The benefits of cloud-native technologies, such as microservices and serverless architecture, are vast and include:

  • Faster deployment. DevOps teams can move quicker with cloud-based technologies. They can deploy and iterate on applications more rapidly, which is highly desirable in fast-paced organizations and industries. 
  • Improved scalability. Often, cloud-native technologies are easier for DevOps teams to scale and this therefore makes them highly advantageous.
  • More flexibility. Cloud-native technologies offer DevOps teams more flexibility, allowing them to create and deploy applications using a wide range of tools.  
  • Cost-effective. The reduced need for physical infrastructure is often more cost-effective, enabling DevOps teams to save money and focus on other priorities. 

As organizations seek to streamline DevOps operations, improve efficiency, and undergo digital transformation, cloud-native technologies and serverless architecture will thus continue to lead the way.

cloud computing

3. Infrastructure as code

Infrastructure as code (IaC) is another trend in DevOps that’s here to stay. It involves managing infrastructure using the same tools that are used for managing code. This means it’s easier for teams to automate the former and maintain consistency in their infrastructure configurations. 
When combined with a multi-cloud approach, the result is standardization across multiple resources or applications, streamlined infrastructure, and greater consistency across platforms, which in turn enhances the user experience.

4. Low code/no code applications

Low code/no code (LCNC) applications use minimal coding and allow developers to create and manage apps quickly and easily. LCNC solutions continue to change the DevOps landscape because they:

  • Enable developers to quickly build applications.
  • Streamline DevOps by including monitoring and resource management tools.
  • Speed up innovation.
  • Reduce the workload for professional developers. 
  • Enable developers to act quickly on customer feedback.

With all these benefits, it’s no wonder that LCNC is a DevOps trend you need to know about to stay ahead in the new year.

5. The use of data analytics

Another key trend that’s only getting bigger in 2024 is the use of data analytics. Using effective analytics tools can continually improve performance and help give stakeholders a better understanding of their investments. Not only that, but DevOps teams, investors, and stakeholders can use data-driven insights to make better strategic decisions. 

Better DevOps decisions lead to cost-effectiveness, better-quality applications, and increased uptake. For example, teams might use analytics to optimize software development processes by providing real-time data and feedback about these. 

Or perhaps performance analytics are required to identify and analyze issues, allowing DevOps teams to continually improve their output and, therefore, the user experience.

laptop metrics

There is an ongoing need for DevOps teams to understand and analyze the development and performance of their applications. As a first step, they may seek out data lake examples when considering their handling and analytics practices. The benefits of effective analytics are huge and, in today’s rapidly developing world of digital technologies, this need will only continue to grow.

6. An increased focus on security

With rapid advances in digital and cloud-native technologies, it’s no wonder that there continues to be an increased focus on security. As well as a need for enhanced data protection as we move into 2024 and beyond, DevOps teams need to consider:

  • Application security. Teams will see an increased need to build security processes into application development. As technologies advance, so do security risks. Implementing these practices as part of the development process will become commonplace. This is referred to as DevSecOps. 
  • Cloud security. As we discussed earlier, there’s currently a surge in cloud-native technologies and infrastructure. It goes without saying that DevOps teams will have an increased focus on cloud security as these technologies develop and become more widely used. This may include data encryption, app configuration, or access controls. 
  • Compliance. With a growing focus on security in DevOps comes a growing focus on compliance practices. An IP phone service, for example, will need to meet GDPR protocols. DevOps teams will find a continuing and growing need to ensure they’re compliant with ever-developing industry regulations and standards.

To enhance security and streamline compliance processes, consider using a tool that allows you to create electronic signature solutions for important documents.

As organizations seek to protect their applications, data, and systems against cyber security threats, the need for a greater focus on DevOps security and compliance will grow. This is likely to lead to an increased need for DevSecOps specialists.

Final thoughts

As we head into 2024, it’s essential to stay ahead of these six trends. Of course, with a rapidly evolving field such as DevOps, it is impossible to predict exactly how the landscape will develop. 

However, the trends outlined above certainly provide an insight into what the future of DevOps is likely to hold. As cloud-native and AI technology continues to evolve, so will it. The technological shifts mean that more organizations will embrace DevOps to meet their business needs and help them undergo a successful digital transformation. 

If there’s one thing that’s for sure, it’s that DevOps itself is going nowhere.


6 Things to Know About Prompt Engineering in 2024

Technology has always had a way of rapidly evolving, often faster than we can keep up. Just look at how far tech has come in the last decade. 3G and 4G networks burst onto the scene. Smartphones became more popular, and by 2022, the average household was home to 22 digital devices.

Fast-forward to 2023, and we’re in the era of AI. Tools like ChatGPT, Dall-e,, and more have thrown a curveball at traditional business processes, forcing businesses to adapt faster than they’d probably like.

In this article, we’ll be focusing on the concept of prompt engineering in AI. We’ll cover everything from its definition to how it’s being used to help businesses streamline their processes and what to expect in 2024.

What is a prompt?

mobile chatgpt

A prompt is a piece of text you enter into an AI program (like ChatGPT) to perform a specific task. A prompt can be anything from asking it to describe an ETL pipeline to more complex tasks like creating full stories and summarising complex articles or documents for easy reading.

The quality of the prompt determines the quality of the response. It’s like having a fantastic interviewer ask great questions to an interviewee. The more specific the prompt, the more specific (and often better) the response and output.

What is prompt engineering?

As mentioned, the quality of the prompt determines the quality of the response. Simple questions like asking, “What is a unified data warehouse?” will generate a response that answers this question in a way that the AI believes is best. What we mean by this is that no further parameters have been set.

For example, if this same question was asked but an additional prompt saying, “Please answer in a conversational tone, in less than 150 words and use short, snappy sentences,” then ideally, its output would be tailored to these instructions.

In essence, prompt engineering is about understanding AI’s architecture to create prompts that consistently deliver the best results and outputs.

How prompt engineering works

chatgpt home screen

Understanding every intricacy of how prompt engineering works would be difficult to summarise in one post, especially considering it’s constantly evolving and has only been around for a year! 

We outlined how a prompt like “Please explain what a medallion data pipeline is” will generate a straightforward answer, but how does it do this?

At its core, prompt engineering can be narrowed down to four key principles.

Model architectures

A model architecture refers to the design and structure of an artificial intelligence model. ChatGPT uses a model architecture known as a “transformer”—it’s like a blueprint for how a computer understands language. Bard (Google’s version of ChatGPT) is also built on a transformer architecture. Both ChatGPT and Bard are Large Language Models (LLMs).

Both allow these separate AIs to handle tonnes of complex information and data as well as understand and interpret context through self-attention mechanisms (the process of weighing the importance of different words in a sequence relative to each other).

To create the “best” prompts—and get the best responses—prompt engineers will have to have a solid understanding of model architectures.

Model parameters

The sheer number of parameters that AI programs like ChatGPT and Bard have is immense. 

We’re talking millions, if not billions, of parameters. The more the prompt engineer knows about a model’s parameters, the better they will be at creating a prompt that generates the best outcome.

Training data

LLMs learn from huge sets of data, breaking input into smaller parts called tokens. The way we break them (like by words or byte pairs) affects how the model understands requests. For example, changing how a word is split can give different results. 

The entries “spaceship” and “space, ship” would bear different results for an AI image generator. One may be of a spaceship in space. Meanwhile, the other would likely generate an image of a seafaring ship in space.

Temperature and top-k insights

When AI models create responses, they use methods like temperature setting and top-k sampling to control randomness and diversity. 

Temperature influences how varied the outputs are; a higher temperature makes responses more diverse but less accurate, whereas top-k sampling limits the choices to the top-k—most likely next words—adding control. 

For example, with a high temperature, asking a model about colors might give a broader range like “blue, red, sunny.” In contrast, a lower temperature might offer more focused responses like “blue sky.” 

Prompt engineers tweak these settings to get desired results, finding a balance between creativity and accuracy in AI-generated content.

What to know about prompt engineering in 2024

2023 was an AI whirlwind—from using it to automate some of the more mundane tasks in our jobs to transcribing conversations in a small business VoIP phone system to diagnosing brain tumors. There’s no doubt AI has made much of our working lives easier.

As we step into 2024, the world of AI and prompt engineering is showing no signs of slowing down. Here are some of the main things to know about prompt engineering going into next year.

1. It’s not going anywhere

AI is here to stay, and that’s good news for prompt engineers. Companies have already started making changes to their hiring practices with AI in mind, with roles in prompt engineering high on this list.

According to a McKinsey survey, around 7%​​ of people surveyed whose companies started using AI said they hired someone with prompt engineering skills in the last year.

Following this, more than two-thirds expect their organizations to increase their AI investment over the next three years. However, this isn’t necessarily bad news for current employees, as many companies will reskill their existing employees as part of their career development path as opposed to replacing them.

2. Demand across industries will increase

As more and more people accept AI’s integration with our day-to-day lives, the demand for prompt engineers will likely increase. The best SaaS management platform will use prompt engineering to summarize meeting notes and update projects, and this will continue to expand into other industries like healthcare and entertainment.

3. There will be more prompt engineer career options

There are already jobs being posted on websites, including LinkedIn and Indeed, revolving around the subject of prompt engineering. As AI continues to develop, the need for people who know how to use it properly will follow suit. 

Industries like digital marketing and advertising will likely be searching for experienced prompt engineers going into 2024. The role itself will likely be broad and take many forms. For example, some prompt engineers may be asked to work with chatbots to enhance their support functions to provide better responses and services to real customers. 

Plus, on the freelance front, prompt engineering will likely join the freelancer category. Just as there are freelance designers and copywriters, there will now be room for freelance prompt engineers.

The demand for this will likely be high, especially for businesses that choose to outsource their prompt engineering needs instead of hiring new staff.

4. It will continue to deal with ethical implications

chatgpt prompting

Despite the apparent benefits that AI has brought with it, there are also plenty of problems. Data safeguarding issues, real-world bias, discrimination, inaccurate information, and general ethical concerns still somewhat tarnish AI’s reputation.

As we move forward in 2024, it is crucial that prompt engineers (and those that use them) follow best practices and guidelines to ensure ethical prompting. 

5. There will be both challenges and opportunities

As with any new piece of tech or trending interest, it will present challenges and opportunities. One of which will be learning how to use and navigate the increase of prompt engineering programs. ChatGPT, Bard, and Bing Chat are among the leaders of this technology, but since their introduction, more spin-offs have popped up.

Prompt engineers will need to have their fingers on the pulse to ensure they don’t get left behind when it comes to learning and adapting to this ever-evolving technology. 

Another issue will be the battle between bias and fairness. Prompt engineers will have to be skilled writers and researchers to accurately assess the output of a prompt. For example, a chef with no experience wouldn’t be able to distinguish a great dish from a bad dish due to lack of experience. 

Creators of AI platforms must also play a bigger part in ensuring that the outputs of their creations are as accurate and unbiased as possible.

6. Adaptation is crucial

Prompt engineering isn’t going anywhere (at least not in 2024). As more models are introduced into the world, more industries will adapt them into their strategies, and the need for prompt engineers to effectively utilize them will increase.

Prompt engineers will make sure these models are easy to use and relevant to the user. Plus, as more and more people begin to use AI, prompt engineers’ roles will evolve. 

For example, they’ll likely be tasked with creating easy-to-use interfaces, crafting user-friendly prompts that anyone can understand, adapting to future trends, and ensuring AI works for its users.

Prompt engineering: Bridging the gap between humans and AI

women in front of a mac

AI burst onto the scene last year and completely changed the landscape of technology, revolutionizing how we approach tasks, make decisions, and interact with information. 

While prompt engineering can be seen as a branch of AI, let’s not underestimate the importance of its role. Prompt engineering essentially creates the bridge between human intent and AI’s understanding of that intent. Without the right prompts, we’re less likely to obtain the right responses.

With the focus and demand for LLM models sure to increase going into 2024, prompt engineer jobs and skillsets will likely follow suit. At the core of this is effective communication, and without a seasoned prompt engineer at the helm, achieving this will become difficult.