Most growth-minded businesses have large volumes of data on their hands that come from an ever-increasing number of different data sources. This paves the way to understand and visualize data better. As a result, data and business intelligence teams can monitor just about every area of the business.
From a technology infrastructure standpoint, older databases like MySQL and Hadoop aren’t particularly mashup friendly. This is mainly because the way they store data can potentially lead to table scan bottlenecks. For this reason, data teams need to use next-gen database infrastructure that allows for easier data mashups.
What data mashups are and why they’re important
A data mashup is simply an integration of two (or more) datasets in a single graphical user interface. The application combines disparate data sources to form a data mashup. It’s worth noting that a data mashup can be made in a single data source environment by combining data from multiple database tables.
Data mashups are most commonly used by data analysts and business users who study dashboards and reports. They empower users to create their own metrics and reports that best serve their organization’s specific needs. Users can also repurpose the data to get answers to previously unanticipated questions but they can also combine them with new datasets to leverage self-service business intelligence.
This means that different types of team members in different types of companies can gain insight from the information they can only see via data mashups.
For example, sales and marketing teams can use purchase funnels to learn more about their organization’s customer acquisition process and identify ways to improve it. One way to do this via a data mashup is to measure the number of prospective customers at each stage of the purchase funnel and what percentage moves from one stage to the next down the funnel. For each stage of the purchase funnel, you’ll have to pull data from different data sources such as your CRM, social platforms, email automation tool, and web traffic analytics.
Smart city projects typically consist of a set of applications with data sharing options. Most of the services that are offered by these projects are actually mashups that combine data from several data sources such as Google Maps and proprietary databases that include logs from traffic lights and public works sensors.
Why older databases are limited in their mashup friendliness
Traditional relational databases – like MySQL, Oracle, SQL Server, Access, and Hadoop – store data in consecutive rows.
This structure is best for transactional and operational systems that require concurrent insertions. It also works for row-based queries that don’t require aggregations or many tables to be joined. Put simply, relational databases will give you realistic query response times if you don’t have to create many joins.
However, since data analysis often requires users to merge data from multiple, disparate sources, relational databases reach their limits when dealing with these sorts of queries. The problem with this storage technique is that it can lead to a table scan bottleneck. For this reason, they’re limited in terms of mashup friendliness.
The workaround for this is to use next-gen database infrastructure that can pre-aggregate the data in order to reduce the number of calculations that occur in real-time.
Here are three next-gen databases that make data mashups easier for data teams.
Founded in 2018, TriggerMesh is a cloud-native integration platform provider that’s built on Kubernetes. It integrates with AWS EventBridge to connect on-premise applications with cloud infrastructure.
This makes it possible to create cloud-native data mashups.
Data teams can use TriggerMesh to connect SaaS, cloud, and on-premises applications with serverless and cloud-native architectures. This is particularly useful for specialized services that would benefit from being integrated with Amazon’s cloud services.
Created by BI platform Sisense, ElastiCube is a data mashup-friendly, next-gen database infrastructure solution. It’s designed to utilize computing resources efficiently which allows it to enable a single commodity server to crunch terabytes of data on relatively inexpensive hardware while still being able to serve a large number of concurrent users.
In addition to this, with ElastiCube, data is stored on the disk rather than in the RAM. This means that the only limit on its size is the size of available disk-storage on the machine. Its query processing engine loads and unloads data to and from the RAM on-demand. Its high-performance analytics database utilizes a column-store in which the data is stored directly on the disk as separate columns instead of consecutive rows.
In this way, ElastiCube data stores are designed to withstand extensive querying required for business intelligence applications.
Here’s how data mashup with the ElastiCube engine works. ElastiCubes enable you to use data from multiple, disparate sources (including physical locations) and then merge, manipulate, and query the data as a consolidated dataset.
It’s incredibly easy to mashup multiple data sources. This is because it’s made up of fields that have corresponding values in other fields. In this way, every field coming from every database table can be analyzed quickly.
Invented by Carlo Strozzi in 1998, NoSQL is a mashup-friendly database infrastructure in which data is not structured in fixed relational columns. In other words, the NoSQL database lets users access, store, and retrieve data in a way that isn’t modeled in tabular relations the way it is with a traditional relational database like MySQL.
A NoSQL database involves various types of data structures. It has a cluster-friendly, non-relational structure that can easily handle large volumes of data.
Data can be stored in data schemas that have a flexible structure. As a result, it’s able to handle large amounts of data in an efficient and cost-effective way.
Next-gen database infrastructure fuels innovation
Next-gen database infrastructure makes it possible for businesses to use data from multiple, disparate sources in order to get answers to previously unanticipated questions and generate reports that best serve their organization’s specific needs.
By using next-gen database infrastructure, your organization can easily handle terabytes of data in a systematic and economical way.