Introducing Data Lake Analytics for Fintechs

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Big data is a booming market, but one that many businesses fail to capitalize on to extract real value from their information.

In the Fintech space, you don't just have to deal with high volumes of data. You must also juggle stringent security and governance requirements, protect your IP, integrate with existing systems, policies and legacy applications, or may work within a multi-tenant environment.

All of these components complicate your big data landscape, making it difficult for Fintechs to achieve the agility they require to make data-driven decisions in a cost-effective manner.

So, how can you analyze your data that is stored in the cloud quickly, securely and at low costs?

A data lake could support your digital banking and regulatory and compliance initiatives, effectively allowing you to turn your raw data into real-time insights. A data lake is essentially a centralized repository where you can store all of your structured and unstructured data.

Once you have implemented your data lake, you can run a range of advanced analytics without needing to structure that information, making it a simple and cost-effective solution for many businesses.

For example, you could implement machine learning, predictive analytics, data discovery and profiling to your data lake to extract insights from this information. This is a more sophisticated solution than a data warehouse, where you can usually only run batch reporting, basic business intelligence and visualizations of your data.

However, the cost and risk of integration associated with a data lake implementation are often too high for many Fintechs.

Introducing Data Lake Analytics

Data Lake Analytics (DLA) is a high-performance, interactive analytics service from Alibaba Cloud that takes a different approach, mitigating many concerns that Fintechs face when considering a data lake.

Compared with conventional analytics platforms, DLA is based on a serverless architecture. This means that it is a ready-to-use service with no infrastructure setup or long-term investment required. DLA is also billed based on your actual usage, making it a cost-effective solution.

What's more, DLA supports elastic scaling and any upgrades can be performed with zero service downtime, allowing for more agile and rapid product iteration.

Our DLA solution is primarily designed for developers, data scientists and business analysts working in the Fintech space. It provides a built-in ETL (Extract, Transform and Load) functionality, eliminating the pre-processing of your data.

DLA is a flexible solution and can be used to query data stored in a number of our services, such as objects in OSS, relational data in RDS, and key-value pairs in Table Store.

Unlike other data lake solutions, DLA provides a security environment, running on an isolated service based on our Virtual Private Cloud (VPC) technology with fine-grained access control.

In short, DLA can provide Fintechs with a secure means to extract real value from their data, minimal expense from a financial and resource perspective. Click here to find out more.

To learn more about how technology is impacting the financial services industry, download our ebook, Big Data and Blockchain: Innovative Fintech in the Cloud.

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Introducing Data Lake Analytics for Fintechs

Introducing Data Lake Analytics

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