What is a searchable database?
A searchable database is an organized collection of records that can be queried or retrieved based on a specific field. By inserting the field, the database should allow end-users to pull out all relevant information. For example, a customer searchable database can include each customer’s name, email, phone number, job title, job company, and so on. You can simply search for a customer’s name to obtain the rest of the information.
In short, the purpose of searchable databases is to centralize all relatable data in a single place and allow end-users to get the information they need without having to compile complex queries or build technical infrastructure.
Choose a tool to build one
There are a variety of options that you can choose from on the market. Before diving into the available solutions, you can think of the following questions to help you make a better decision:
- Does your data come from multiple sources, such as spreadsheets, databases, or APIs?
- How large is your database? If it’s small now (like less than 100k rows), will it become larger and larger in the future?
- Does your database need to be updated at a certain time?
- In addition to simple keyword search, do you need more advanced search functionalities?
- Do you need to share the database with other people?
Basically, if you don’t have too much data (maybe less than 1M rows) and you are proficient in Excel or Google Sheets, you can consider using spreadsheet programs to create a searchable database. All you need to do is to create several input boxes and set up VLOOKUP functions to return the matching values based on the inputs.
Although spreadsheets allow you to create a search system with complex logic, they are not databases. They are not built for storing and processing large amounts of data. When your database grows to a certain extent, your Excel may take a very long time to return the results. Moreover, spreadsheets are easy to edit, so anyone may change the formula or data unintendedly. Consequently, your database may get errors, and missing values due to the easily editable nature of a spreadsheet program.
Relational database systems
Relational database systems, such as MySQL, PostgreSQL, are common and scalable methods to build a database. No matter how large your data is, it can be stored in a structured format. The problem is that you need to have enough database knowledge and coding skills to set up the entire system. The knowledge includes designing the schemas, defining relationships between tables, and building the connection between data source and database. Once the database is constructed, you need to know how to write SQL queries to retrieve the data you need. Moreover, most of the relational databases are running on your local server. If your database is too large, it may take several minutes to return the search results depending on your computer’s performance.
Cloud DB management platforms
Cloud DB management platforms, such as Acho, are more ideal when it comes to creating a searchable database. They can store and process big data like a relational database system, but they have a user-friendly GUI that allows you to do searches with no codes. In addition, they can utilize cloud computing to speed up data processing. Even if your database has more than 10 million rows, you still can get the search results instantly. Second, in addition to spreadsheets, they support importing data from various sources, such as relational databases, third-party apps, and APIs. You can put all data in a single place and use them to enrich your database. Third, they have built-in features that allow you to transform and clean data without writing codes. Thus, you can customize the database into any table you want.
Create a searchable database
In this article, I will use Acho to demonstrate how to build a searchable customer database.
1. Import data from various data sources
Your business may use several different tools to manage your customer data. For example, you may use MySQL to record customer information, Google Sheets to manage CRM, and Stripe to deal with customer payments. The first step is to import all data that your database needs.
2. Combine your data to enrich your database
Once all data is imported to Acho, you can create a new project and import all datasets to the project. Within the project, you can combine these datasets into a single table by using Join or Union.
3. Set up schedulers to automate data sync
You can create schedulers to update your database. The schedulers will retrieve the latest data from your data sources and run all actions that you have applied. Thus, you don’t need to recreate the database whenever you get new data.
Explore your database
Do a quick search through the search bar
If you want to do a quick search, you can simply type a keyword in the search bar at the top of the headers. It will return all of the results that include the keyword in the given column.
Create a complex search by using the Filter action
Alternatively, you can set up a set of filters to narrow the search results. Based on each column’s data type, the filter will provide different logics such as “is or after” for dates, “contains” for texts, “larger than” for numbers, etc.
After applying your filters, you can even store them as your favorites so you can revisit them later on.
Share your database
A searchable database can be valuable and beneficial to lots of people. Currently, Acho supports two methods to allow you to share the database with others.
Invite other people to your project
You can create a database that is only visible to you. However, if you want to share the database with someone else, you can invite them to your Acho project. Once they log into Acho, they can create their own searches to look for data they want.
Publish the searchable database on your website
If you have your own website, another way is to embed the database on your webpage. To do so, you can click the export button and select “Website (Embed)”. Then, click generate Embed Link to get HTML codes, so you can embed the table on your website. (Note: to offer a better experience at the front-end, the maximum number of rows of the embedded table is 5000 rows.)