Philipp Krenn
Make Your Data FABulous
#1about 7 minutes
Understanding the CAP theorem for distributed systems
The CAP theorem states that a distributed data store can only provide two of three guarantees: consistency, availability, and partition tolerance.
#2about 3 minutes
Introducing the FAB theory for datastore tradeoffs
The FAB theory proposes another set of tradeoffs for data stores, where you can only pick two of three attributes: fast, accurate, or big.
#3about 7 minutes
How terms aggregation trades accuracy for speed
Elasticsearch's terms aggregation may return inaccurate counts by default because each shard only considers its top local results to improve performance.
#4about 8 minutes
Inconsistent relevance scores in distributed full-text search
Full-text search relevance scores using TF-IDF can be inconsistent because inverse document frequency is calculated per-shard, not globally.
#5about 2 minutes
Using a single shard to ensure data accuracy
Forcing an index to use a single shard guarantees accurate aggregations and relevance scores by eliminating distributed calculations, but sacrifices horizontal scaling.
#6about 1 minute
Why you must consciously choose your data tradeoffs
It is crucial to understand and explicitly choose the tradeoffs in your data systems, like those in the CAP and FAB theorems, to avoid unexpected behavior.
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Optimizing performance with advanced data distribution methods
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Introducing the core principles of Elasticsearch
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Recapping Kafka's capabilities for real-time data feeds
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Modern data architectures and the reality of team size
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Distributing data using shards and replicas
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AUTO1 Group SE
Berlin, Germany
Intermediate
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ELK
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