Understanding and mitigating risk is at the forefront of any financial services institution. However, as previously discussed in the first blog of this two-part series, banks today are still struggling to keep up with the emerging risks and threats facing their business. Plagued by the limitations of on-premises infrastructure and legacy technologies, banks until recently have not had the tools to effectively build a modern risk management practice. Luckily, a better alternative exists today based on open-source technologies powered by cloud-native infrastructure. This Modern Risk Management framework enables intraday views, aggregations on demand and an ability to future proof/scale risk management. In this two-part blog series, we demonstrate how to modernize traditional value-at-risk calculation through the use of Delta Lake, Apache SparkTM and MLflow in order to enable a more agile and forward looking approach to risk management.
Apache® Spark™ News
In the non-contractual scenarios within which most retailers engage, customers may come and go as they please. Retailers attempting to assess the remaining lifetime in a customer relationship must carefully examine the transactional signals previously generated by customers in terms of the frequency and recency of their engagement. For example, a frequent purchaser who slows their pattern of purchases or simply fails to reappear for an extended period of time may signal they are approaching the end of their relationship lifetime. Another purchaser who infrequently engages may continue to be in a viable relationship even when absent for a similar duration.
Cloud computing has fundamentally changed how companies operate – users are no longer subject to the restrictions of on-premises hardware deployments such as physical limits of resources and onerous environment upgrade processes. With the convenience and flexibility of cloud services comes challenges on how to properly monitor how your users utilize these conveniently available resources. Failure to do so could result in problematic and costly anti-patterns (with both cloud provider core resources and a PaaS like Databricks). Databricks is cloud-native by design and thus tightly coupled with the public cloud providers, such as Microsoft and Amazon Web Services, fully taking advantage of this new paradigm, and the audit logs capability provides administrators a centralized way to understand and govern activity happening on the platform. Administrators could use Databricks audit logs to monitor patterns like the number of clusters or jobs in a given day, the users who performed those actions, and any users who were denied authorization into the workspace.
R is one of the most popular computer languages in data science, specifically dedicated to statistical analysis with a number of extensions, such as RStudio addins and other R packages, for data processing and machine learning tasks. Moreover, it enables data scientists to easily visualize their data set.
This is a joint engineering effort between the Databricks Apache Spark engineering team — Wenchen Fan, Herman van Hovell and MaryAnn Xue — and the Intel engineering team — Ke Jia, Haifeng Chen and Carson Wang.
Managing risk within the financial services, especially within the banking sector, has increased in complexity over the past several years. First, new frameworks (such as FRTB) are being introduced that potentially require tremendous computing power and an ability to analyze years of historical data. At the same, regulators are demanding more transparency and explainability from the banks they oversee. Finally, the introduction of new technologies and business models means the need for sound risk governance is at an all time high. However, the ability for the banking industry to effectively meet these demands has not been an easy undertaking. Traditional banks relying on on-premises infrastructure can no longer effectively manage risk. Banks must abandon the computational inefficiencies of legacy technologies and build an agile Modern Risk Management practice capable of rapidly responding to market and economic volatility through the use of data and advanced analytics. Recent experience shows that as new threats emerge, historical data and aggregated risk models lose their predictive values quickly. Risk analysts must augment traditional data with alternative datasets in order to explore new ways of identifying and quantifying the risks facing their business, both at scale and in real-time.
Pandas user-defined functions (UDFs) are one of the most significant enhancements in Apache SparkTM for data science. They bring many benefits, such as enabling users to use Pandas APIs and improving performance.
A common data science internet of things (IoT) use case involves training machine learning models on real-time data coming from an army of IoT sensors. Some use cases demand that each connected device has its own individual model since many basic machine learning algorithms often outperform a single complex model. We see this in supply chain optimization, predictive maintenance, electric vehicle charging, smart home management, or any number of other use cases. The problem is this:
Guest Blog by Niranjan Nataraja and Karthikeyan Rajendran of Nvidia. Niranjan Nataraja is a lead data scientist at Nvidia and specializes in building big data pipelines for data science tasks and creating mathematical models for data center operations and cloud gaming services. Karthikeyan Rajendran is the lead product manager for NVIDIA’s Spark team.
We’re excited to announce that the Apache SparkTM 3.0.0-preview2 release is available on Databricks as part of our new Databricks Runtime 7.0 Beta. The 3.0.0-preview2 release is the culmination of tremendous contributions from the open-source community to deliver new capabilities, performance gains and expanded compatibility for the Spark ecosystem. Using the preview is as simple as selecting the version “7.0 Beta” when launching a cluster.