You’re building a new product and are looking for the best data source. Or maybe you need a better API for your current application or service. You have three options — build your own API, use a free public API, or pay for a third-party API. Your use case will likely dictate which option you choose, but to help you decide we’ve broken down all the pros and cons of each API approach — starting with building your own.
Building an API: Pros & Cons
Organizations usually build their own API when they have the in-house expertise and don’t think they’ll find the customization they need from an external source. However, the investment can quickly become overwhelming if you don’t have a plan and think through the long-term impact. Here are the pros and cons of building an API.
Pros of Building Your Own API:
Customization: By building your own API, you have complete control over its functionality and features. This allows you to tailor it to the specific needs of your business or application.
Integration: You can build your own API to integrate with other parts of your application or software stack seamlessly.
Scalability: With a custom API, you can ensure that it scales with your business needs and can handle increased traffic or usage over time.
Security: Building your own API allows you to implement security measures specific to your business needs and data.
Cons of Building Your Own API:
Cost: Building and maintaining an API requires significant time and resources, which can be expensive.
Expertise: Building an API requires expertise in software development, web services, and other related fields. If you or your team lacks these skills, you may need to hire additional staff or outsource the work.
Time to market: Building an API can take a significant amount of time, which may delay the launch of your application or service.
Maintenance: Once your API is up and running, it will require ongoing maintenance and updates to ensure it stays secure and performs optimally, which can take away from other important activities.
Using a Public API: Pros & Cons
Public APIs allow developers to access data and functionality from other applications without having to build it themselves — for free. Public APIs can certainly be beneficial, but because they’re free, there are usually some serious implications for using them. Here are the pros and cons of using a public API:
Pros of Using a Public API
Speed: Public APIs are typically easily accessible –– perfect if you only need an API for a one-off purpose. And this can be a major advantage for start-up businesses that are looking to quickly launch new products or features, but without the cost.
Flexibility: While a public API may not be flexible in terms of features and functionality, it can be great if you’re looking to test the water with a particular API before you want to make the full investment of building or buying a better option.
Cost savings: Using a public API saves you money as you won’t have to pay for the development and maintenance of an API. This can be a significant cost saving if you’re on a tight budget.
Cons of Using a Public API
Security risks: Public APIs can be a security risk, as they provide access to data and functionality that could be used to harm businesses or their customers. If you’re a large business, it’s unlikely that a public API will adhere to your privacy standards.
Reliability: Public APIs can be unreliable with outages or other disruptions. Often they’re not designed for long-term use and whoever developed them may no longer be maintaining them. This can be a major problem if you plan to rely on a public API to power your product or service.
Performance issues: Public APIs can suffer from performance issues, such as slow response times or downtime if they don’t have a dedicated team in place to help with support. This can have a significant impact on your user experience and the overall performance of your application.
Limited control: Using a public API, you’ll have to work with the underlying infrastructure and recognize you won’t be able to customize it. This could also cause more challenges during the integration process.
Using a Third-Party API: Pros & Cons
Unlike public APIs, third-party APIs are APIs as a product and are provided by companies and organizations for a cost.
The prevalence of APIs as a product has increased due to the demand for support when it comes to API development and maintenance. Data providers specializing in APIs have more expertise in developing APIs since it’s their core business. As a result, third-party APIs offer many advantages, but there are also some downsides to consider.
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Time savings: Using an existing API saves time by allowing developers to reuse resources, rather than building everything from scratch. Developers can focus on the core functionality of their application or service instead of working on maintaining parsers and proxy networks.
Scalability: Third-party APIs provide access to large datasets that would be difficult, if not impossible, to collect on your own. This data can include anything from SERP results to product prices to reviews and more. And since the API is built as a product, it’s usually able to withstand large volumes of requests.
Easy integration: Since third-party APIs are being sold as a product, they have to be able to integrate with a variety of data ecosystems. This can be highly beneficial if you’re working with multiple environments.
Better user experience: Third-party APIs can help provide a better user experience by allowing developers to spend less time on the maintenance of the API and more time on creating additional features and content in their applications – which makes their applications more valuable and engaging to users.
Cons of Using a Third-Party API:
Security risks: Similar to public APIs, not all third-party APIs are built ethically. Some can pose a security risk to both the provider and the user. However, most third-party providers know this is important if they want to be able to charge for their API, so they typically encrypt requests and take other security measures to protect you and your data.
Dependency on third parties: Using an existing API means you depend on a third party to provide the service. If the provider goes out of business or decides to change the terms of their API, this could have a significant impact on your application and business.
Limited control: Like public APIs, using a third-party API you have limited control over the underlying infrastructure. This means that you may not be able to customize the API to meet your specific needs, and you may be limited in terms of the functionality you can provide to your users.
Ultimately, the decision to build an API will depend on your business needs, budget, and resources. If you have a skilled development team who have additional time to commit towards this effort – then you should go for it. However, if you lack the necessary expertise, time, or resources, it may be more cost-effective to use a third-party API provider.
If you find the task of building your own API too demanding or your current solution isn’t cutting it, we can help. We understand how it feels to be burdened by the expensive, time-consuming process of building your API – or to be left with a public API that’s underperforming when it comes to your needs. We recognize this is a challenging process for businesses to undertake. That’s why we continue to expand and enhance our collection of reliable, robust, real-time APIs.
APIs have become increasingly important to the success of businesses. Many technology products, applications, and systems depend on them to perform their duties. And the number of API requests continues to grow. As a result, poor API performance can slow processes down and impact the user experience — which results in a loss of revenue.
By measuring your API performance, you can identify areas where it can be improved and ensure that it is meeting the needs of your business or users.
Where APIs Generally Lack In Performance
The most critical aspect of an API is speed — for a few reasons:
First, a slow API can cause delays for users. When users have to wait for an API to respond, they may become frustrated and abandon the application.
Second, a slow API can increase costs for developers. If developers have to wait for APIs to respond, they may have to spend more time and resources building and testing their applications.
Third, a slow API can damage the reputation of the company that hosts it. If users have a bad experience with an API, they may be less likely to use the company’s products or services in the future.
It’s also important that an API is reliable and efficient. Any downtime can result in the same negative outcomes as a slow API. While it’s fairly straightforward to measure the speed and reliability of an API, measuring its efficiency is a little more complicated. An efficient API is going to have functionality that supports easy integration and scalability. Here are some steps to get a full understanding of your API’s performance when it comes to speed, reliability, and efficiency.
How to Measure API Performance
Sites and applications are constantly updated to keep up with new technologies and enhance the user experience. Because applications are constantly evolving, APIs need to be closely monitored and frequently improved to keep up with changes.
If you’ve been using a third-party API or your own in-house API for the past year or more and haven’t done an evaluation of it, now is the time. Realistically, you should complete these steps every few months to see if your current API program is performing well:
1. Assess Metrics
Begin by assessing the performance of your API across a variety of metrics. Choose the metrics that are most relevant to your specific needs. You can start with these:
CPU Usage
Memory Usage
Uptime
Rate Limits
Latency
Errors Per Minute or Error Rate
Request Per Minute (RPM)
Excessive CPU usage, memory usage, downtime, and errors can mean an issue with the server, system health, or code.
2. Gather User Feedback
Talk to your users or internal stakeholders who leverage the API and its output. Are they experiencing issues or having trouble using it? Are there gaps in the data? User complaints or inaccuracies in the data may indicate the API is outdated. On the other hand, having to format or modify its output signals an issue with the functionality or efficiency of your API.
3. Run Performance Testing
Performance testing involves simulating a large number of users accessing your API and measuring how long it takes to respond. This method for measuring API performance is helpful if you want to anticipate potential issues with your API — particularly as API request volume grows and your application needs to scale.
Keeping tabs on the metrics, user feedback, and performance of your API will make it easier to notice any consistent, significant issues that signal a performance issue. But if you find there is an issue, there are several ways to help improve performance.
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There are many ways to improve API performance, and some will depend on your specific application. But here are a few tips:
Use a caching mechanism: Caching saves time by storing frequently accessed data in a temporary location so that it doesn’t have to be retrieved from the database each time.
Optimize your code: Make sure your code is well-written and efficient. This can help your API to respond more quickly to requests.
Use a load balancer: A load balancer distributes requests across multiple servers, which can help to improve performance if you have a lot of users.
Use a content delivery network (CDN): A CDN is a network of servers that are located around the world. When a user requests a resource from your API, the CDN will deliver the resource from the server that is closest to the user. This can help to improve performance if your users are located in different parts of the world.
Use a monitoring tool: A monitoring tool can help you to track the performance of your API and identify any areas where it can be improved.
Test your API regularly: Testing your API regularly can help you to identify any performance issues before they affect your users.
By following these tips, you can improve the performance of your API and ensure that it meets the needs of your users. However, if you find the task of maintaining your own API too demanding or your current solution isn’t cutting it, we can help.
We understand how it feels to be burdened by the expensive, time-consuming process of building your own API – or to be left with a public API that’s underperforming when it comes to your needs. We recognize this is a challenging process for businesses to undertake. That’s why we continue to expand and enhance our collection of reliable, robust, real-time APIs.
Why would an SEO limit themselves to some keywords, when all keywords are available?
Historically, it’s been a matter of resources and capacity. The more data points you introduce, multiplied by the number of keywords you’re tracking, the faster it becomes unmanageable. When using a typical SEO platform to track keyword data, it requires prioritization and selection of a more limited keyword list that leaves off low-traffic queries and details about things like rich results.
That’s where big data, in the raw output form that can be parsed by BI tools, comes in to expand a keyword universe.
We’ll walk through the process of what raw, big keyword data looks like, how it’s given the structure that allows it to be manipulated, and the qualities your business should look for when choosing a BI tool that will allow you to turn data into insights.
Why use Big Data for SEO
SEOs or digital marketers have a finite amount of keywords for their brand that they have the capacity to track. Typically, this is determined by the keywords that are known to drive traffic to a site, with the threshold being a significant number of clicks.
What happens when you have a very niche longtail keyword that only generates a nominal amount of clicks? It’s probably not going to move the needle on your bottom line, so it gets left off the list of priority keywords to track. Then you have a few more very niche keywords that generate just another handful of clicks.
For many businesses, this list of “insignificant” keywords can grow into the thousands, where the aggregate of all those nominal clicks suddenly has a lot more power over your business than you originally expected. For example, listen to Wil Reynolds of Seer Interactive recount his experience with discovering that 300,000 previously ignored keywords with ~1 click each accounted for 92% of his client’s traffic.
The problem then becomes the ability to scale the keyword research process to track exponentially more keywords, as well as more SERP data points, like various rich results.
When the capacity of an SEO tool is limited, it makes sense to prioritize and limit keyword lists or the search features you track. However, moving to big data where the data is in its rawest form—and able to be pumped into any analytic environment—allows businesses to expand their keyword universe to all keywords, not just a “representative” sample size.
To start, you will have to decide what this analytic environment will be, and how you will process and manipulate it without a typical SEO platform. Traject Data can be directly connected to the BI tool that your company likely already uses to make other data-driven decisions. Next we’ll dig into the process the data undergoes so it can be parsed into useful data sets and made ready for that BI tool.
How Traject Data can be used
With more SERP data points now available for tracking, use cases for leveraging the data have increased as well. Here are a few things your business can do with Traject Data:
Inform organic SEO and content creation strategy.
Track keyword rankings across millions of keywords.
Identify industry trends and evolving SERPs.
Blend with other data sources to identify revenue-generating keywords.
Optimize advertising campaigns based on evolving search engine algorithms and search intent.
Monitor local performance across geo-location specifications.
Raw data from the Traject Data SERP API comes in the form of JSON files that describe any given SERP, including the search features, ranked websites, and anything else that appears for that particular query. Imagine you query “teriyaki and wok” and you’re shown this SERP. You’ll notice that result #1 is a standard blue link, not a local pack, a video, or any other type of search feature.
The output from the Traject Data SERP API for this query would look like this:
Underneath “SERP” in the JSON file, each ranking position will be labeled by number and list out the attributes of that ranking position. In the “teriyaki and wok” case, you can see the data for the link, the description, and which types of search features do and do not take up position #1 for that query.
If you were to keep scrolling, the same data would be listed for position #2, #3, and so on for that query’s SERP.
This more comprehensive set of SERP data points allows SEOs to increase their control over all the possible rich results at their disposal. Tracking this data will show rich results they’ve won and lost to competitors, search features that are new to a keyword after Google makes an algorithm update, and other data points that will help them home in on all of their high-return traffic opportunities.
These are some of the most common rich results that we provide data on. You can click through on each to learn more about how you can leverage them.
We’re your premier partner in web scraping for SERP data. Get started with one of our APIs for free and see the data possibilities that you can start to collect.
How is this data converted into something I can manipulate and draw insights from?
The information in the JSON files is pretty extensive to look through for just one SERP. This raw output would be almost impossible to manipulate without building logic, or using a tool that can sort through it to access what you need and create more relevant datasets.
The Traject Data SERP API JSON files are stored in Amazon S3 or similar. There, the data points from the JSON files are structured and tagged into individual fields so that they are able to be queried, sorted, and more easily analyzed. After the JSON files have been given this structure, using a product like Amazon Athena gives you the ability to query the database and pull the selection that’s interesting to you.
This staging ground and additional ability to organize the data creates a way for previously unmanageable amounts of data to be pulled into subsets that are relevant for a certain report or analysis.
Leveraging a BI tool
The dataset is now ready to be connected to your BI tool which will allow you to visualize it for easy understanding. From there, you can build reports and combine it with your business’s other data, like sales and marketing data.
BI tools are an important cornerstone to this process because the visualizations allow users to discover new insights with the time and expertise (or bias) that would typically go into creating pivot tables to investigate a hypothesis.
With the various tools that are compatible with Traject Data, users can toggle between different types of visualizations much more deftly than building out a new analysis manually each time. Expert level BI users can manipulate the data into more complex visualizations, but beginner level users can still glean a multitude of insights from the visualizations the tool suggests, once the data finishes populating.
Traject Data keyword data is able to connect to all of the most common BI tools on the market. To make a decision on which BI tool to use, consider the required skill level, pricing, and capabilities below.
Tableau connects and extracts the data stored in various places, and is touted as being able to pull data from just about any platform imaginable. It can extract “simple” databases such as excel or pdf files, to more complex databases like Oracle or AWS.
After ensuring thorough data access, Tableau emphasizes its ease of content discovery by allowing users to organize resources by project, recommending relevant data, allowing ‘go back’ with revision history, and enabling search.
Here’s an example Tableau sales dashboard. You’ll notice that showing the insights is highly visual, and that analytics can be performed almost instantaneously as users change any setting on the dashboard. This viz uses Tableau’s clustering feature to uncover insights in the sales data across the United States.
Tableau divides their product tiers by implementation and user needs. First, your business will need to decide whether you’d like to host Tableau on-premise, in the public cloud, or have Tableau host your server. Then decide how many of each of the following user type you need:
Creator: This is a data analyst role who will load and standardize data. Every Tableau account is mandated to have at least one Creator
Explorer: general business user role, can make and edit visualizations
Viewer: can view and interact with visualizations Explorers and Creators have made
Tableau’s pricing differs between users of on-premise/public cloud and Tableau hosted, and bills annually. For on-prem, select the number of licenses you need:
Creator: $70 USD/user/month
Explorer: $42 USD/user/month
Viewer: $15 USD/user/month
Pros
Quick and easy representation of big data which helps in resolving the big data issues
Can be used with no coding or development knowledge
Easy to understand, drag and drop interface
Can use pretty much any data source imaginable—Tableau integrates with over 250 applications
Has an additional platform for cleansing and prepping data before it’s loaded and analyzed
There’s a mobile app with comparable functionality
Has a heavy community presence for learning in forums
Cons
Some users criticize its collaboration capabilities: The notification functionality is simple, and only an admin can configure scheduled email subscription. Users can use custom Python to create trigger-based notifications, but that practice isn’t baked into Tableau.
Can be a fairly expensive tool based on your business and needs
To leverage the full capabilities of Tableau, users report a much steeper learning curve that that of other BI tools.
Operates with a multitude of products that all handle a different aspect of the data analytics process. Some companies may enjoy the customized experience — it’s not for you if you’re looking for one tool that gives all users a transferable experience
The bottom line
Tableau is the top of the food chain for BI tools in terms of quality of analytics and visualization. However, for many users, the cumbersome process of piecing together multiple Tableau products and deploying on-prem may be too complex—and too expensive.
Looker is the data visualization tool owned by Google. Its main differentiator is its 100% browser-based experience, which eliminates the need for installation and maintenance at the same level as a tool like Tableau. Looker also emphasizes shareability in many of their opening statements, referring to the fact that the browser-based experience allows for link sharing, as opposed to sharing with files.
Like most other BI tools, Looker asserts that any novice can use it and that in-depth SQL knowledge can create more intricate reports, but isn’t strictly necessary. However, there does seem to be a significant investment in publishing lectures, documentation, and learning puzzles to appeal to Looker users across disciplines and learning styles.
With Looker, data analysts can use a language called LookML to create mini-applications that “add efficiency and power to data exploration.” Looker explains that the LookML syntax simplifies the development of powerful models and enhances the capabilities of SQL. According to them, “this language-based approach leads to faster query execution, and optimizes performance along the way.”
Below is an example of a Looker E-commerce dashboard that visualizes different data points from a sample company.
Looker is unique from other BI tools in that it doesn’t publicly share its pricing, reasoning that it’s customizable for small, medium and large businesses. SoftwareConnect estimates the following prices:
Generally $300-$500/user/month, up to 10 users
$50/user/month after 10 users
Pros
Emphasizes pre-built templates or blocks to speed up setup time and move more quickly to custom reports
100% browser-based experience eliminates the need for desktop client software installation and maintenance, and allows for link-based sharing of content – makes collaboration “frictionless”
Consistent platform experience for all users; for instance, they can complete tasks without shifting from one desktop tool (i.e. Tableau Desktop), to another (i.e. Tableau Prep Builder), and then to the web to complete specific tasks.
Users can leverage the ML code (that most say is fairly easy to learn) to help produce applications and optimize queries
Still works for those with no SQL knowledge, as they have a rich database of videos and learning materials
Cons
Some users report problems with load time, even calling the wait for a visualization “a run-in with the spinning wheel of death”
Users report lessened functionality and viewability when using the mobile app
If third-party estimates are correct, Looker can actually be more expensive than Tableau (which has gotten negative reviews for price) in some cases
Out-of-the-box dashboard templates can be incredibly useful, but some report difficulty attempting to customize them
Completely hosted means no options for fundamentally changing the tool besides waiting for a new release
The bottom line
If you don’t want to install and maintain an on-prem solution, Looker’s hosted platform has data visualization capabilities that rival or exceed other BI tools. You’ll be able to enjoy a report sharing experience that isn’t cumbersome, but watch out for load times and the inability to change the functions of the tool itself.
Domo Business Cloud, a low-code data app platform, refers to itself as: “the platform that allows everyone to drive action from data.” As with other top data visualization tools, users can combine data from disparate sources, and according to Domo, without needing duplication or pre-transformation to use.
Domo also includes collaboration tools that let teams communicate directly on visualizations with messages and annotations, in addition to scheduled updates and benchmarking alerts similar to those of other BI tools. There isn’t an on-prem deployment option, but Domo touts the cloud-based solution as a baked-in method to help your business scale indefinitely.
Below is an example of a Domo dashboard that uses Traject Data and shows the breakdown of all the different types of search features that appear in the chosen dataset.
Plans and Pricing
Domo’s editions are primarily divided by number of users, support level, and administrative options. The enterprise edition offers unlimited storage, data sets, personalized data permissions, and advanced admin controls.
Domo previously (until 2019) offered two plans: Professional ($175/user/month) and Enterprise ($250/user/month). They have since removed pricing from their website.
Pros
Regarded as one of the fastest-operating BI platforms
Provides templates based on business function
Many users enjoy the UI more than that of other BI tools, citing its “vibrance.” If you frequently use reports to woo stakeholders, this might be meaningful
Allows for team communication and collaboration directly on the platform
Seamlessly blends data from hundreds of sources, the reason Domo has been able to gain traction on Tableau
Offer 1-Click Apps: pre-built connectors that allow users to upload data without relying on IT
As a cloud-based solution, implementation tends to be shorter than on-prem solutions like some Tableau
Offers a full mobile app
Cons
Can be difficult to configure connectors from different sources all at once
No on-prem deployment option
If you plan to create many similar reports, some users complain that you can’t copy a report and make slight changes to the copy, as it will update the original
The bottom line
If your business favors cloud-based over on-prem and will be using the tool for extensive communication and collaboration while building visualizations, Domo is a great option.
You can expect that Power BI, the data visualization tool owned by Microsoft, will play nicely with other Microsoft products like Azure and Office. In fact, this is one of the first points that the Power BI website confirms.
These other Microsoft products, such as Power Apps, Power Automate, and Power Virtual Agents that create an “end to end” experience give users a feeling similar to picking and choosing certain Tableau products for a more customized experience, not the singular consistent experience of Looker and Domo.
Power BI connects to most types of on-premise databases, and they have a large and growing list of cloud-based connection options as well. Users report that it also works well for companies that don’t have a data warehouse solution, because Power BI acts as a facilitator that enables data set processing.
It has around 16 different chart types, which is a good, middle-of-the-road amount if you want to represent your data in a variety of formats and visualizations.
Below is an example of a Sales and Marketing Dashboard created with Power BI.
Power BI has 2 plans. The Pro plan is the most inexpensive BI solution out of comparable tools in the market, but the Premium plan can get pricey.
Power BI Pro
Self-service and modern BI in the cloud
Collaboration, publishing, sharing, and ad-hoc analysis
Fully managed by Microsoft
$9.99/user/month
Power BI Premium
Enterprise BI, big data analytics, cloud and on-premises reporting
Advanced administration and deployment controls
Dedicated cloud compute and storage resources
Allows any user to consume Power BI content
$20/user/month or $4,995/capacity/month for dedicated cloud compute and storage resource with annual subscription
Pros
The most inexpensive tool of its caliber on the market
Like the other tools, Power BI connects to hundreds of data sources and can read data from Microsoft Excel and text files like XML and JSON
Boasts a mobile app with comparable functionality
Anyone with moderately advanced Excel skills is likely to have an intuitive experience, as users report the functionality as being similar
Microsoft releases updates to Power BI monthly and listens to the user community. If you submit suggestions for improvements, other users can rank the suggestions
Plays well with Excel and is has easy export functionality
Cons
Limits the size of datasets that you can pipe in – can try creating multiple queries to process the entire data set or shift to Power BI Premium
On-premises reporting is only included in the Power BI Premium package, which can add additional costs
Some users report the design is not intuitive and the UI/UX isn’t as pleasant as other tools
Custom visuals don’t seem to be configurable – limitations to what can actually be changed if you want to optimize a visual
No solution for data scrubbing, so data needs to be cleaned before using Power BI
The bottom line
If you don’t have a lot of budget and won’t be using datasets that exceed the size limit, Power BI will give you top-tier analytical capabilities—and may even listen to you on the updates you’d like to see.
Final Thoughts on Choosing a BI Tool for Traject Data
Because Traject Data works with all major BI tools on the market, decide on a tool that fits your business best with the above information in mind.
Once you’ve chosen a BI tool, you’re prepared to follow these 3 steps to begin visualizing the additional SEO data points our SERP API provides:
Pull the JSON files for each keyword you want to track with the Traject Data SERP API.
Use S3 and Athena (for example) to make a database from your JSON files that you can query and filter to create a more relevant dataset.
Connect your dataset to the BI tool that works best for you and your organization.
Complete these steps, and you’ll have visualizations of endless keyword data to inform your SEO strategy and business decisions as a whole.
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