All businesses generate data in this or another manner. Ideally, it should be turned into a resource for stronger decision-making. Let’s review a few ways of how to do that.
Data Pipelines
Data pipelines are the structured pathways through which data travels from one point to another. The beauty of these is that they let businesses collect, process, and distribute information. That should be especially useful when you’ve got data coming from multiple sources and would love to organize it into a unified repository.
What you do is that you reach out to data engineering services and ask them to develop a data pipeline for your business. As a rule, they’d offer to choose between two types of pipelines: Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT).
| Pipeline type | ETL | ELT |
| Order of steps | Data is extracted from the source and transformed into the necessary format. Then, it’s loaded into a data warehouse. | Data is extracted and loaded into the storage. Then, it’s transformed as needed within the storage environment. |
| Use case | Ideal for structured data and environments with defined schemas (e.g., traditional databases). | Suitable for handling large volumes of unstructured data (e.g., within cloud-based data lakes). |
| Performance | slower | faster |
| Cost efficiency | more cost-efficient for small data sets | cost-efficient for big data |
Now, let’s see how these can be used in different industries.
- Retail
Retailers can use ETL pipelines to consolidate data from various POS systems. Thanks to this, they’ll perform stock analysis and trend forecasting faster. This setup is a good fit for demand prediction and inventory management.
- Healthcare
Hospitals and healthcare networks use ELT pipelines to collect and store vast patient and operational data. They thus gain real-time insights into patient trends and resource allocation.
- Finance
Banks and financial firms can use ETL pipelines to process customer data and transaction history securely. They can likewise detect fraud patterns and ensure compliance with regulatory standards with the help of these pipelines.
Data Lakes & Data Warehouses
What brings these two solutions together is that they are for storing and managing business data. What sets them apart is the purposes they serve.
Data Lakes
These store massive amounts of raw, unstructured data from various sources. The latter can include IoT devices, social media feeds, and other streams. The best thing about data lakes for a business is that it can hold large datasets at low costs. They are thus ideal for exploratory data analysis and ML applications.
Data Warehouses
These are almost the opposites of data lakes. The data they store is
- structured
- cleaned
- processed data.
Such data is ready for reporting, analysis, and business intelligence. Data warehouses are necessary when your business needs reliable insights on demand (e.g., historical performance trends).
| Criteria | Data Lakes | Data Warehouses |
| Flexibility | flexible raw data storage | require predefined schemas, ideal for structured reporting |
| Cost-efficiency | cost less due to lower storage and processing requirements | cost more but provide faster, structured querying |
| Data accessibility | allow for quick data exploration but data scientists must sift through unstructured information | direct access to cleaned data, suited for immediate analytics by non-technical users |
Now, who and when would use these? A marketing firm, for instance, could use a data lake to collect raw data from multiple campaigns (social media, email, web). Later on, it would refine this data for sentiment analysis and trend exploration.
As to a data warehouse, an e-commerce company might use it to maintain historical sales data. Thanks to it, teams will be able to create automated reports on revenue, customer retention, and sales trends.
Data Analytics
There’s a whole spectrum of solutions here. This involves all techniques that help businesses to derive insights from their data and make informed decisions. So with the help of a good data engineering service, you can get a custom solution built for your needs. This solution may combine different types of data analysis.
- Descriptive Analytics
This is the first step of data analytics. Here, the program summarizes historical data so that your company can understand past performance. For example, a retailer might use descriptive analytics to analyze last month’s sales trends and find out which products sold best.
- Diagnostic Analytics
So the descriptive part is about the “what.” The diagnostic one examines the “why.” If we return to the example with the retailer, at the diagnostic stage, they should find out why those products sold best.
- Predictive Analytics
You’ve found out the “what,” then, the “why,” and now it’s time to decide what to expect based on that. At least, your custom tool can forecast trends and challenges. For example, that same retailer might use such a tool to anticipate peak sales times.
- Prescriptive Analytics
And finally, when you know what to expect, you’ll want to know what to do about it. That’s the most advanced type of analytics. It can recommend specific actions based on the insights gained. The retailer will use this tool to find out which adjustments they need to make before the peak sales period (not only that, of course).
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So is Investing in robust data engineering solutions like these worth it? Most likely yes but you should find this out from the data engineering service you plan to work with. As a rule, they’ll, first and foremost, consult you on which type of solutions will be the most cost-effective in your situation, and only after it, will build this solution for you.
