Companies are building data applications for a variety of uses, from marketing apps that provide customer insights, to IoT apps that handle device feedback, and data analytics apps that process both historical and near real-time data.
However, developers often face obstacles when building, designing, and supporting applications that need to parse large volumes of information. To address these challenges, companies are building their data applications on cloud platforms.
With unlimited storage capability, robust backup and security infrastructures in place, applications in cloud platforms do not contend for resources and allow companies to have continuous and safe access to the data and insights they need. Upgrades and software updates are also built into the system, which helps in reducing maintenance costs. With capabilities including virtually unlimited and automatic scalability, concurrency, near-instant elasticity, and support for structured and semi-structured data, organizations are looking to migrate their data applications and processes to the cloud.
Here are five ways businesses can benefit from building high-performance data applications in the cloud:
1. Developing a 360-degree view of the customer
According to Data Reportal's Digital 2019 Spotlight Report, Southeast Asia will continue to enjoy double digit growth in e-commerce spend. With the surge in e-commerce adoption, retailers in the region have an opportunity to move to a digital-first approach for customer engagement. Customer data is typically fragmented across many systems and consists of different data types. For example, recent customer purchase history is stored in operational databases while unstructured and real-time event data, such as a clickstream goes through complex data pipelines. On the other hand, demographic data and intent, typically obtained from third parties, need to be combined with customer data and this usually requires integration and data duplication. With the help of a cloud data platform, businesses can easily build their customer profile by generating aggregated data on products, audiences, purchase attributions, and user activity without copying or moving data. By building a 360-degree view of the customer, companies can develop new product segments and create personalized offers.
2. Handling Internet of Things (IoT) device data in near real-time
Ingesting, storing, and analyzing massive amounts of device data for IoT applications often overwhelms traditional data pipelines. Unstructured data from IoT requires a conversion phase that often disrupts the ingestion process. Additional delays and expenses can also arise because raw device data is usually stored in cloud object storage and must be moved to a data warehouse for analysis. With applications in the cloud platforms, ingestion is optimized through buffering of real-time data, batch staging and automatic chronological data processing. These processes ensure that applications analyzing large volumes of time-series data from IoT devices respond in real-time.
3. Combining historical and current data for security and application health checks
Developers of security and application health analytics apps often need to evaluate data to predict anomalies, detect threats, and respond proactively. However, combining large amounts of historical and current data for analysis makes real-time data ingestion difficult. Furthermore, storing large volumes of data for extended periods can be costly. With unlimited storage capacity, cloud data platforms solve these challenges and offer a way to identify security threats and monitor application health. Security analytics in cloud platforms invoke queries against stored and aggregated new data to detect suspicious behaviors. As systems detect unusual activities, IT teams can then be alerted of the issue and respond accordingly.
4. Bringing data together for machine learning (ML) models
Constructing accurate ML models require large volumes of clean, historical data. To support each experiment, copies of entire data sets must be created. Data cloning is a costly and lengthy process, which can limit the number of experiments conducted and hurt model accuracy. To support near real-time predictions, application developers need to query fresh data with fast response times. Using the data stored in cloud platforms, IT teams can create models with ML platforms and available libraries. Once models are set, they are deployed on the training platforms or in a separate process to support predictions without the need to create data clones.
5. Embedding analytics in data-intensive applications
Developers face many challenges in embedding analytics in data-intensive applications. Performance issues and concurrency limits occur as the number of users or the complexity of queries increase, resulting in possible downtime and poor user experiences. Additionally, ingestion of semi-structured data requires dedicated engineers who can maintain the pipeline to avoid disruptions when schemas change. To alleviate the performance impact, developers often over-provision compute, which hurt profit margins by paying for idle resources. Cloud data platforms support in-app analytics by storing historical data in virtual warehouses. These virtual warehouses isolate application workloads and auto-scale compute resources when needed.
Conventional data platforms struggle to deliver on their fundamental purpose, which is to enable applications to work with any data, without limits on scale, performance or flexibility. With the help of cloud data platforms, applications builders can solve the challenges organizations face to get the most out of enterprise data.
Geoff Soon, managing director for South Asia at Snowflake authored this article.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Photo credit: iStockphoto/Viktoriia Hnatiuk