Kickstart Guide to Web Data Integration
More data is better data, right? Whilst that’s technically true, it would be better to see, wider data integration leads to better insights and business potential. But that doesn’t quite roll of the tongue, you know?
However, since you’re reading a guide on data integration from web data scraping experts, we’re going to go ahead and assume you’re a very data-driven and savvy individual 😉 So let’s talk about how you can expand and supplement your existing data: by gathering additional data from the web!
In this guide, we want to explore the benefits of web data, how to ensure data quality and how to implement data integration solutions as part of your business intelligence processes. This will include:
- An introduction to web data integration
- The importance of adding web data into your business processes
- Key components of your data integration strategy
- Common data integration techniques
- What data integration solutions you need
- How to ensure effective data governance
- Best practices for scaling your data integration business processes
We’re also going to assume you know a little about data infrastructure already. We won’t explain a data lake from a data warehouse – if you’re looking to build on your data science capabilities, you should probably already know the benefits of structured data vs unstructured data already. But if you want to improve data quality and gain an even greater, unified view? Read on…
What is Web Data Integration?
Web data integration is the process of collecting, transforming, and merging public web data – information gathered from external websites – into your internal business systems.
In other words, web data integration takes the wealth of information available online and combines it with your proprietary data to unlock deeper insights, drive smarter decisions, and help you stay ahead of the competition.
So how could such data integration work in an actual business context? Lets look at a few examples:
- Imagine an eCommerce company that scrapes competitor pricing and product availability from retail websites. It can then use this information alongside its own sales figures to adjust its pricing and promotions with a greater deal of accuracy compared to an internal pricing engine alone.
- In the travel sector, companies can use hotel reviews and ratings from numerous sites to prioritize the best options for customers, finding new opportunities that internal data wouldn’t reveal alone.
- What about a hedge fund that scrapes news headlines and financial sentiment data to better understand factors outside of internal trading methods. It sounds fancy, but it’s no joke: sentiment analysis in financing is becoming an increasingly influential factor for decision-making.
- When it comes to real estate, investment firms can gather property listings and prices to determine the value of different regions, which can then influence their internal purchasing and leasing strategies at a neighbourhood-level.
Why is Web Data Integration Important for Modern Businesses?
You don’t need to be a data scientist to know that data integration from multiple sources is always better. With multiple data sources, you’re both gaining a wider view and paving the way for real-time analytics.
For example, your internal systems probably contain a lot of information. With your CRMs, ERPs and other analytics dashboards, you likely have decent oversight on customer data and all the things that are happening inside your business.
With web data, you can fill in the missing half – what’s happening outside your business. For example:
- Gain real-time data regarding your competition
- Understand public opinion through sentiment analysis
- Track your own brand’s visibility and potential
- Monitor trends with greater clarity
- Build custom machine learning models unique to your industry
But let’s also not forget that we’re talking about data integration here. Data silos aren’t effective – it’s only through combining data that you unlock the most strategic insights.
Key Components of a Data Integration Strategy
Real data scientists know that effective data analysis doesn’t happen by accident. You need a deliberate and well-structured strategy. And this starts before data extraction.
- Define your business objectives: What do you want to achieve? Do you have certain metrics or KPIs you wish to track more thoroughly, or are you building a single source of truth? Knowing what out comes you need will influence what data sources you need, how you transfer data and even whether or not you need real time data integration.
- Identify data sources: Let’s face it, you likely already know what internal operational data you have available. When consolidating data from multiple sources, both inside and out, you should focus on the external aspects. What information do you need, and from which third-party sources do you need it from?
- Establish data governance: Sure, it’s about as fun as it sounds, but it’s critical. There’s a good chance that you likely already have some protocols in place for internal data processing, but it’s worth revisiting when public web data is included – but we’ll touch more on this later.
- Know your tech stack: We’re pretty sure you haven’t been running your business in the dark, so you likely already have a number of tools available, ranging from data storage systems, analytical dashboards and maybe even some extraction tools. If you’re new to web data scraping, then you will need to consider these additional elements. What proxies do you need for data scraping? Or perhaps you’ll use a Web Scraper API? And once you have the data, do you have the stack to transform data to work with your existing data sources?
- Automation, monitoring & maintenance: The bigger your data integration ambitions become, the more quickly you move out of manual possibilities. Automated data is the future, but technology isn’t perfect. Integrations break and one tool’s update might be incompatible with another. It’s the same with web data: sites change. So ensure you have automated monitoring systems along your data pipelines to alert you to any unexpected breaks in your data flows.
Sure, we skipped a few steps here, as we assume you’re fully aware of security and other aspects, like how to store customer data compliantly. We’re web data nerds, not cybersecurity experts 🙂
Common Data Integration Techniques
There’s more than one way to integrate data, and each comes with its own trade-offs. The right choice depends on your current setup, goals and/or the type of data you’re working with – especially if it involves web data.
| ETL (Extract, Transform, Load) | The classic approach – extract data, transform it into your intended format, and then load it into the target system. ETL prioritizes data quality and structure, so its ideal when accuracy is more important than speed. |
| ELT (Extract, Load, Transform) | In this version, you extract and load the data first, and only transform later once it’s in your intended data system. ELT is ideal for high-volume environments where transformation can be handled at sale after ingestion. |
| CDC (Change Data Capture) | Rather than pulling entire datasets all the time, CDC only updates what’s been changed. It’s mostly used for syncing internal data silos in near real time and with minimal resource usage. |
| API Integration | When data providers offer API, it’s often the go-to option. It’s clean and reliable when it’s an option. |
So how does web data fit into this? Generally speaking, the raw data you extract is going to need some level of transformation. You should only scrape the data you need – not only for ethical reasons, but also to cut down on transformative work later – but even then, you’ll likely need to format it to suite your data system.
While web scraping API is available, this alone isn’t going to transform data for you. So, when integrating web data, in most cases, we recommend some combination of ETL and API integration. Of course, every case is different, and we’re always happy to help our customers with a challenge.
Ready For Scraping?
If you just want to start scraping and have a goal in mind, try our web scraping API. You get the raw data, we take care of all the technical challenges 💪
Choosing the Right Data Integration Tools
We won’t dwell on this too much, but we’re sure you’re aware of the different tools available. Whether you want to use something off-the-shelf or go completely custom, knowing the answers to the above questions will certainly help.
What we will say, however, is that you shouldn’t over-invest too early.
It’s easy to get caught up with an expensive and flashy modern data integration platform, when a simpler tool – or even a well-structured script – might do the trick. Start by solving your most immediate data integration needs, and scale from there.
Your data integration tools should serve your strategy, not the other way around!
But to help you choose a data integration tool, remember that you can generally split them intro three categories: open-source, commercial and custom builds.
| Type | Pros | Cons | Best For |
| Open-source | – Free to use – Highly customizable – Large community support | – Can require significant setup and maintenance – Less user-friendly UI | – Technical teams comfortable with DIY – Cost-sensitive projects |
| Commercial | – User-friendly – Comes with support & documentation – Quick to deploy | – Licensing costs – May be less flexible for custom workflows | – Mid-to-large businesses – Teams with limited engineering resources |
| Custom Build | – Tailored exactly to your needs – Full control over architecture | – High upfront development cost – Ongoing maintenance required | – Complex, large-scale use cases – Organizations with in-house dev teams |
Understanding the Data Integration Process
Proper data integration is not a one or two step process. It’s a continuous process – and one you really have to understand if it’s your job.
- Identify sources – research and choose what datasets you will collect from
- Data ingestion – the data collection step
- Data transformation – turning unstructured data into structured data, as needed
- Data consolidation/federation – combining data from multiple sources. More on this below.
- Storage – data lakes, data warehouses? The cloud or custom servers? The data has to go somewhere.
- Analytics & activation – at this point, you can actually start to use the data stored 😉
- Ongoing monitoring & governance – protecting your data and ensuring its integrity.
Data Consolidation vs Data Federation vs Data Ingestion
Let’s stop for a second and consider the different terms for collecting and combining data. For the most part, when we talk about data integration, we’re referring to data consolidation, but you can also consider data federation as well, in specific use cases. Data ingestion, however, is really a stepping stone to more effective data integration solutions.
Data consolidation
Arguably the most common approach to data aggregation, this method sees you consolidate data from multiple sources into a singular, central repository. If you’re putting everything into a date warehouse or data lake, and then using traditional business intelligence or analytical tools to access data from this repository, that’s data consolidation.
Data federation
Although similar, data federation differs in that you do not integrate data into a single whole, but rather leave them apart and use a virtual layer to access data from multiple sources. This is often used where real-time data is important, as you can access the information without waiting for a cyclical update of the entire data repository.
Data ingestion
Finally, data ingestion focuses mostly on simply acquiring data. It can done via either real-time or batch processing, but it often simple adds raw data into your data warehouses or lakes.
Often, data ingestion is the first step in acquiring data, but your businesses generally look to move to either data federation or consolidation – and the formatted data that comes with it – as quickly as possible.
Data Governance Considerations in Web Data Integration
Data integration, as with all things data-related, also comes with a lot of governance obligations. Even if it wasn’t required from a legal standpoint, it’s just a good idea!
We trust you’ve already got your internal data policies on lock, so let’s focus on the additional challenges of public data integration and acquisition.
Source Transparency
Whether it’s internal or external, you should know exactly where every piece of data is coming from. This means not just tracking the what, but also the where and how. For web scraping, this means keeping the URLs, timestamps and collection methods.
It’s crucial for data lineage and auditability. If you want long-term trust in your insights, you need to be accountable.
Quick tip – you can use metadata tagging when ingesting scraped data to retain source-level context. You’re welcome 😉
Ethics & Compliance
There are two types of web scraping – ethical web scraping and the kind we don’t condone, support or talk about. If you’ve made it to a section about compliance and ethics, we assume you’re one of the good ones.
You can click the above link for a full guide, but we’ll give you a quick summary here:
- Take only the data you need
- Don’t take any personally identifiable information – or intellectual property
- Respect the Terms of Service for any site you scrape from
- Make sure you use ethical proxies and tools
- If a website offers an API, use that first
And, of course, respect the websites you use. That means respecting rate limits and not overloading servers. Really, don’t be a d**k.
Ethical Proxies
Our proxies are 100% ethically-sourced and only used responsibly. We ensure this by only working with responsible clients!
Data Accuracy & Integrity
We said it before: websites change. When sites update or adapt, it can disrupt the information you’re extracting. Make sure you implement validation rules, freshness checks and alerts for scraping failures.
It’s not an everyday occurrence, but you’ll want to know at what point data was compromised or simply incomplete. If you followed the first step on source transparency, then you’re already in a good place.
Access Control & Internal Policies
Limiting access is a key part of any data governance. It’s more important when dealing with customer data or other secretive or personable information. While web data is “public”, you still want to control who can transform it – and what transformations are allowed – to control how it gets modified and interpreted downstream.
Cost & Resource Tracking
Web data is not “free” just because its publicly available. You still have to consider the costs of your proxies or web scraping API, alongside any additional storage and transformational costs.
We know a little something about this, so we’re always happy to help 😉 It’s not a simple case of more proxies equates to more power. There are many factors to consider, such as how frequently you need to rotate proxies, which type of proxies are the most effective and how to generally get the most resource-efficient results.
Best Practices for Building a Scalable Data Integration Strategy
We’ve shared a lot of advice, but we’re almost done! Let’s break down some of the key practices for data integration when implementing web data.
Keep Up to Date with Web Data
When it comes to the fully internal pipelines of your data integration system, you have a much broader range of control. You’re able to influence how, when, and where the data is collected, so its formatting and acquisition is unlikely to change without internal approval.
The internet, however, does not answer to you 😲 Public web data can change. Sites can change how data is formatted or how frequently it is updated. Perhaps more importantly, they can also update their anti-bot protections and make it harder for automated data scraping processes to acquire the data in the first place.
What we’re saying is… keep an eye on your public data. If you fully automate your pipeline and don’t check in, you won’t notice the inconsistencies until you’re analysing the data after the fact.
Automate Wherever Possible
If it’s manual, it’s going to be slow. Much of your data extraction – such as proxy rotations and scraping scripts – can be automated. The same goes for transforming and loading data. The majority of your data flows should be automated, leaving your teams to focus on the results… so long as you have monitoring processes and alerts in place.
This is especially true if you’re looking to implement real time data integration.
Be Smart With Proxies
Web scraping isn’t a guaranteed process. You have to be careful to ensure you don’t get your IPs banned or blocked. A smart choice in proxy types and rotations will typically see you through. In some cases, you might face a number of web scraping challenges, ranging from rate limiting through to TLS fingerprinting. But we can help with that!
Build With Scalability in Mind
There’s a good chance you might want to access data on an even larger scale in the future, so think with this in mind. Your data warehouse can expand, but what about acquisition? You can consider parallelism over concurrency, scalable data storage and modular codes to help you go wide when the time is right.
Web Data Integration is the Future
If you made it all the way down here – you must really be passionate about data! We hope this guide to data integration was certainly worth it.
So, if you’re now ready to supplement your existing data sources with web data, you know what to do! And if you’d rather not do it alone, how about a team of expert web scrapers and data engineers? With a little help from us, you’ll be able to extract transform and load in no time 😎
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