In the last half a year, we saw the dawn of digitization in Fintech. Companies have been forced to adapt to the new reality and make partnerships to get access to bigger volumes of data, which may help them provide more value to customers. A seamless integration allows for an advanced user experience that is so required during the lockdown.
What we see in the market now are plenty of integrations between companies of all niches, such as payments, lending, insurance, compliance, wealth management, and so on. The ecosystem has become more and more integrated, as we can see from the data by Fintech Integrations Marketplace. In portfolio management software, as an example, we can see that building integrations with other market players is a trend. The most popular options for integrations are custodians, financial planning tools, and CRMs. Quite naturally, all these data providers may open new opportunities for companies from almost every subniche in Fintech.
Data Integration Challenges
The problem with partnering instead of building your own analog is that the more integrations one adds to a Fintech product, the more complicated the project becomes. When no automation or consistency is in place, it comes too difficult to scale such an application. The lack of these two can be compensated for either by hiring more engineers to handle bottlenecks manually or using tools for making the process of data integration fast, consistent, and uninterrupted.
Moreover, sometimes integration is necessary on a deeper level, such as in the case of a merger. The challenges will also affect technology teams from both sides in such a case, though data transformation and integration will be still a problem that needs to be addressed.
Below you can find several examples of challenges that go hand in hand with data integration and tools that may help you to optimize extraction, transformation, and load processes.
Wrong data input can also affect businesses. To prevent data inconsistencies in your system, check the source data carefully, and avoid exposing your clients.
Connectivity disruption causes data losses and/or damage. To combat this case, it’s necessary to monitor internet connectivity and download progress all the way through.
Redash can help. Redash (https://redash.io/) allows us to connect and query data sources, build dashboards to visualize data, and share them within the company. It allows implementing dashboards that help you see the big, easy-to-digest picture for a deeper understanding of your data processes and better decision-making.
Hadoop. The Apache Hadoop (https://hadoop.apache.org/) software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high availability, the library itself is designed to detect and handle failures at the application layer so it can deliver a highly available service on top of a cluster of computers, each of which may be prone to failures.
Unexpected format changes are undesirable but unfortunately common in this area. Each company wants to grow and provide its customers with new opportunities, so your data providers can change the format they are sending their data to you. In case their communication channels aren’t dialed in, the format change can appear as a surprise to you, which will bring hours of overhead fixing the process to enable work with the new reality.
What Else Can Be Useful When Integrating Data Sources
Xplenty (https://www.xplenty.com/) is a cloud-based ETL solution that provides simple visualized data pipelines for automated data flows across a wide range of sources and destinations. The company’s powerful on-platform transformation tools allow its customers to clean, normalize, and transform their data while also adhering to compliance best practices.
Spark. Spark (https://spark.apache.org/) is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools, including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. Apache Spark is a popular solution for those who implement machine learning and big data analytics into their product because it’s a lightning-fast, unified analytics engine.
CloverDX (https://www.cloverdx.com/) is a rapid end-to-end data integration solution. Businesses choose CloverDX for its usability and intuitive controls along with its lightweight footprint, flexibility, and processing speed. Achieving true, rapid data integration means much more than just raw data processing power. Rapid refers to an end-to-end process that begins the moment a data-related problem is recognized to the point when the data is in the right place and format to be analyzed and monetized.
Custom framework. Sometimes the tools can’t match the specific needs of your business or using them is just not enough. In such a case, building a custom framework for transforming data and testing the results of the process is an option. At INSART, we’ve built one for a project of ours. There’s a guide about ETL processing where we describe how we did it and what results we achieved.
