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WeAreDevelopers LIVE • Nov 10, 2023

Leveraging Real time data in FSIs

Tim Faulkes

Traditional SQL schemas throttle high-throughput financial systems under heavy loads. Discover how intelligent NoSQL aggregation drops real-time transaction latency to sub-millisecond ranges.

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#1 about 3 min

Modern application stacks and real-time data requirements

How developers structure scalable microservice architectures while historically underestimating database performance in real-time use cases.

#2 about 3 min

High-speed data use cases in financial services

Faster dataset ingestion gives fraud, risk, and compliance algorithms more time to execute sophisticated real-time decisions.

#3 about 4 min

Measuring database latency through high percentiles

Why analyzing the ninety-ninth latency percentile matters far more than average response times for high-volume database performance.

#4 about 4 min

Terminology differences between relational and NoSQL databases

Comparing how structured SQL systems and unstructured NoSQL stores handle schemas, namespaces, and records.

#5 about 4 min

Normalization constraints versus nested data aggregations

How embedding related entity sets inside a single NoSQL object eliminates complex database joins and index reads.

#6 about 2 min

Handling NoSQL entity associations with batch processing

Retrieving multiple independent objects efficiently by performing parallel cluster batch reads across separated node distributions.

#7 about 3 min

Meeting CAP theorem consistency rules in distributed clusters

Why distributed databases must choose between availability and system consistency during node network partitions.

#8 about 2 min

Boosting scale and lowering latency natively with NoSQL

Large-scale fraud and identity platforms achieve microsecond latencies by shifting workloads from caching tiers to memory-optimized platforms.

#9 about 3 min

Defining rules and constraints for credit card fraud validation

Setting tight millisecond execution windows to scan vast user transaction histories against deep learning fraud models.

#10 about 2 min

Live benchmarking database transaction throughput and latency

Testing thread concurrency limits by pumping simulated credit swipe data into a relational testing container.

#11 about 5 min

Integrating a Redis database connection into Java applications

Extending a custom Spring application test harness by injecting and configuring a new in-memory NoSQL interface.

#12 about 5 min

Analyzing relational database bottlenecks during heavy SQL loads

Relational cache warmup requirements cause dramatic latency spikes that quickly violate strict real-time query deadlines.

#13 about 6 min

Improving NoSQL performance by bucketing transaction data points

Restructuring event histories into distinct time-based bins minimizes scatter-gather index scans and parallelizes drive reads.

#14 about 2 min

Final takeaways on database selection and community resources

Exploring custom database architectures is necessary because there is no universal engine suited for every enterprise data problem.

Matching moments

3:16 min

Q&A on database vendor lock-in and alternative architectural choices

George Asafev · WWC 2023

2:18 min

Comparing PostgreSQL and DynamoDB for read-heavy storage

George Asafev · WWC 2023

1:32 min

Understanding fast, accurate, and big data store trade-offs

Philipp Krenn · WWC 2022

4:14 min

Recognizing architectural drivers pushing event streaming system adoption

Mary Grygleski Mary Grygleski · LIVE

6:41 min

Audience questions on databases, freeze modes, and monitoring

Cheng Zhang · LIVE

3:04 min

Evaluating NoSQL databases and data conversion bottlenecks

Markus Kett Markus Kett · WWC 2023

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An Phan

Senior Data Infrastructure Engineer @ Hippo Harvest

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