Use Cases
Use Cases
Businesses that react and adapt to customer behavior in real time rely on stream processing software. But stream processing software lacks appropriate correctness guarantees out-of-the-box, whether it's open source or a proprietary cloud service. So engineering teams spend years implementing correctness guarantees themselves, but even the best teams retain errors with impact rates above 0.2% of the total value flowing through the business.
In verticals such as finance, logistics, e-commerce, betting, and stock trading – where operating profits range from 1% to 10% of the total value processed – even a 0.2% loss can represent as much as 20% of operating profit. In other words, 20¢ of stream processing errors per $100 processed is monumental for payment processors, shipping companies, online sportsbooks, e-retailers, quantitative funds, courier services, and similar companies.
Businesses that react and adapt to customer behavior in real time rely on stream processing software. But stream processing software lacks appropriate correctness guarantees out-of-the-box, whether it's open source or a proprietary cloud service. So engineering teams spend years implementing correctness guarantees themselves, but even the best teams retain errors with impact rates above 0.2% of the total value flowing through the business.
In verticals such as finance, logistics, e-commerce, betting, and stock trading – where operating profits range from 1% to 10% of the total value processed – even a 0.2% loss can represent as much as 20% of operating profit. In other words, 20¢ of stream processing errors per $100 processed is monumental for payment processors, shipping companies, online sportsbooks, e-retailers, quantitative funds, courier services, and similar companies.
Businesses that react and adapt to customer behavior in real time rely on stream processing software. But stream processing software lacks appropriate correctness guarantees out-of-the-box, whether it's open source or a proprietary cloud service. So engineering teams spend years implementing correctness guarantees themselves, but even the best teams retain errors with impact rates above 0.2% of the total value flowing through the business.
In verticals such as finance, logistics, e-commerce, betting, and stock trading – where operating profits range from 1% to 10% of the total value processed – even a 0.2% loss can represent as much as 20% of operating profit. In other words, 20¢ of stream processing errors per $100 processed is monumental for payment processors, shipping companies, online sportsbooks, e-retailers, quantitative funds, courier services, and similar companies.
Banking & Fintech
Banking & Fintech
Banking & Fintech
• Underassessment of risk when issuing credit
• Unnecessary investigations into mislabeled fraud
• Uncaught fraud
• Transactions fees lost during system downtime
Logistics & Couriers
Logistics & Couriers
Logistics & Couriers
• Misrouted and missed shipments
• Underutilization of vehicles and staff
• Lack of automation in parcel hubs
• Requests cannot be placed during downtime
Without Ambar
Without Ambar
Without Ambar
The fundamental stream processing model is that streams are logical subsets of events; for example, one stream of events per customer. Each stream must be processed in parallel, with low latency, in order, and without lost or duplicated events. Current solutions fail to implement the stream processing model and its guarantees on an end-to-end basis, i.e. spanning source application, producer, queue, consumer, and destination application.
It is theoretically possible to satisfy end-to-end guarantees by writing extensive custom software on top of current solutions. Nevertheless, in practice there are too many implementation traps and companies do not have the appetite to spend $10m+ on the time and talent required to surmount those traps.
Businesses have evolved from simply storing information inside their databases to reacting and adapting to customer behavior in real time. This shift means that stream processing now impacts companies' entire revenue stream. Thus, engineering teams need formally defined stream processing guarantees, which must apply end-to-end and without errors – otherwise, businesses will lose a significant fraction of their profits.
The stream processing market is cluttered with vendors whose solutions, optimized for analytics with human oversight, falter in machine-driven, real-time decision-making contexts. These off-the-shelf products, while user-friendly, mask critical deficiencies in handling automated, high-stakes scenarios like financial transactions or supply chain management. Beyond the immediate 20% operational profit loss, other solutions prevent businesses from taking full advantage of stream processing, resulting in worse long-term consequences.
The fundamental stream processing model is that streams are logical subsets of events; for example, one stream of events per customer. Each stream must be processed in parallel, with low latency, in order, and without lost or duplicated events. Current solutions fail to implement the stream processing model and its guarantees on an end-to-end basis, i.e. spanning source application, producer, queue, consumer, and destination application.
It is theoretically possible to satisfy end-to-end guarantees by writing extensive custom software on top of current solutions. Nevertheless, in practice there are too many implementation traps and companies do not have the appetite to spend $10m+ on the time and talent required to surmount those traps.
Businesses have evolved from simply storing information inside their databases to reacting and adapting to customer behavior in real time. This shift means that stream processing now impacts companies' entire revenue stream. Thus, engineering teams need formally defined stream processing guarantees, which must apply end-to-end and without errors – otherwise, businesses will lose a significant fraction of their profits.
The stream processing market is cluttered with vendors whose solutions, optimized for analytics with human oversight, falter in machine-driven, real-time decision-making contexts. These off-the-shelf products, while user-friendly, mask critical deficiencies in handling automated, high-stakes scenarios like financial transactions or supply chain management. Beyond the immediate 20% operational profit loss, other solutions prevent businesses from taking full advantage of stream processing, resulting in worse long-term consequences.
The fundamental stream processing model is that streams are logical subsets of events; for example, one stream of events per customer. Each stream must be processed in parallel, with low latency, in order, and without lost or duplicated events. Current solutions fail to implement the stream processing model and its guarantees on an end-to-end basis, i.e. spanning source application, producer, queue, consumer, and destination application.
It is theoretically possible to satisfy end-to-end guarantees by writing extensive custom software on top of current solutions. Nevertheless, in practice there are too many implementation traps and companies do not have the appetite to spend $10m+ on the time and talent required to surmount those traps.
Businesses have evolved from simply storing information inside their databases to reacting and adapting to customer behavior in real time. This shift means that stream processing now impacts companies' entire revenue stream. Thus, engineering teams need formally defined stream processing guarantees, which must apply end-to-end and without errors – otherwise, businesses will lose a significant fraction of their profits.
The stream processing market is cluttered with vendors whose solutions, optimized for analytics with human oversight, falter in machine-driven, real-time decision-making contexts. These off-the-shelf products, while user-friendly, mask critical deficiencies in handling automated, high-stakes scenarios like financial transactions or supply chain management. Beyond the immediate 20% operational profit loss, other solutions prevent businesses from taking full advantage of stream processing, resulting in worse long-term consequences.
Retailers & E-commerce
Retailers & E-commerce
Retailers & E-commerce
• Items out of stock due to missed procurement orders
• Unfulfilled orders
• Cart abandonment due to lag
• Customers cannot shop during system downtime
Sportsbooks & Gaming
Sportsbooks & Gaming
Sportsbooks & Gaming
• Customers place fewer bets at peak times due to lag
• Stale odds due to slow third party data syncs
• Mispriced odds due to processing incoming data in the wrong order
Investments & Trading
Investments & Trading
Investments & Trading
• Trades executed with stale data leading to losses
• Forced liquidation of positions due to margin calls
• Mispriced buy or sell orders
• Lost trading opportunities during system downtime
With Ambar
With Ambar
With Ambar
Ambar provides all the end-to-end guarantees in the stream processing model out of the box. It only requires 14 lines of configuration, works with all programming languages and frameworks, and can be integrated into an existing application within 30 minutes. Besides the guarantees, Ambar enables features that traditionally require much work: reprocessing all historical data or merging streams from disparate sources without compromising any of its guarantees.
Ambar solves stream processing guarantees with provably correct models, which share a point of consensus with customer systems through an outbox table inside the customer’s database. First, customers produce to that outbox table. Ambar will then pull data with minimal latency and push it to customer endpoints in order, in parallel, with exactly-once semantics, with smart error handling, with user-defined filtering, and automatically adapting concurrency according to the destination’s system health.
The case for stream processing is almost obvious these days – change is the only constant and businesses must proactively respond to change, or get left behind. Ambar goes further. By enabling stream processing with guarantees, Ambar eliminates defects and recovers up to 20% of operating profit compared to alternatives, empowering companies to compound their gains into even larger long term advantages.
Ambar provides all the end-to-end guarantees in the stream processing model out of the box. It only requires 14 lines of configuration, works with all programming languages and frameworks, and can be integrated into an existing application within 30 minutes. Besides the guarantees, Ambar enables features that traditionally require much work: reprocessing all historical data or merging streams from disparate sources without compromising any of its guarantees.
Ambar solves stream processing guarantees with provably correct models, which share a point of consensus with customer systems through an outbox table inside the customer’s database. First, customers produce to that outbox table. Ambar will then pull data with minimal latency and push it to customer endpoints in order, in parallel, with exactly-once semantics, with smart error handling, with user-defined filtering, and automatically adapting concurrency according to the destination’s system health.
The case for stream processing is almost obvious these days – change is the only constant and businesses must proactively respond to change, or get left behind. Ambar goes further. By enabling stream processing with guarantees, Ambar eliminates defects and recovers up to 20% of operating profit compared to alternatives, empowering companies to compound their gains into even larger long term advantages.
Ambar provides all the end-to-end guarantees in the stream processing model out of the box. It only requires 14 lines of configuration, works with all programming languages and frameworks, and can be integrated into an existing application within 30 minutes. Besides the guarantees, Ambar enables features that traditionally require much work: reprocessing all historical data or merging streams from disparate sources without compromising any of its guarantees.
Ambar solves stream processing guarantees with provably correct models, which share a point of consensus with customer systems through an outbox table inside the customer’s database. First, customers produce to that outbox table. Ambar will then pull data with minimal latency and push it to customer endpoints in order, in parallel, with exactly-once semantics, with smart error handling, with user-defined filtering, and automatically adapting concurrency according to the destination’s system health.
The case for stream processing is almost obvious these days – change is the only constant and businesses must proactively respond to change, or get left behind. Ambar goes further. By enabling stream processing with guarantees, Ambar eliminates defects and recovers up to 20% of operating profit compared to alternatives, empowering companies to compound their gains into even larger long term advantages.