Distilling Insights @ Appsflyer (Data Architecture)

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The document discusses a company's data architecture and strategy for transforming raw data into useful insights. It outlines the various technologies used at each stage, from ingesting raw data using Kafka to storing aggregated data in a columnar database and performing analytics with Spark. It also touches on evaluating different SQL query engines and machine learning with Spark ML to generate predictive insights for dashboards.

Technology

Distilling insights @
Arnon Rotem-‐Gal-‐Oz
Chief Data Officer

Kafka
Columnar Database
(Redshift- evaluating Vertica)
IMDG
(Ignite - evaluating Geode)
Secor
Spark
Aggregations
SparkSQL
(evaluating
Drill,
Presto)
SQL
SQL
Raw
(sequence
ﬁles)
DW
(parquet
ﬁles)
DM
(Aggregations)
Application
dashboard
Self-serve
BI
(TBD)
Spark
ETL
Spark
Spark
ML
Latest Events
Scoring
exploration
Agg.
logic
Internal
tools
installs clicksinapplaunches
Accounts

Data’s hierarchy of needs*
*With apologies to Maslow
Acted
upon
presented
Distilled
Usable
Accessible
Exist

“Malting”
Just slap SQL on everything
Accessible

RT insights
Predictive
Prescriptive
Dashboards
whatnot
presented

Enrich with foreign
keys before persisting

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The online advertising industry is based on identifying users with cookies, and showing relevant ads to interested users. But there are many data providers, many places to target ads and many people browsing online. How can we identify users across data providers? The first step in solving this is by cookie mapping: a chain of server calls that pass identifiers across providers. Sadly, chains break, servers break, providers can be flaky or use caching and you may never see the whole of the chain. The solution to this problem is constructing an identity graph with the data we see: in our case, cookie ids are nodes, edges are relations and connected components of the graph are users. In this talk I will explain how Hybrid Theory leverages Spark and GraphFrames to construct and maintain a 2000 million node identity graph with minimal computational cost.

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A complex real-time data workflow implementation is very challenging. This session will describe the architecture of a data platform that provides a single, secure, high-performance system that can be deployed in a hybrid cloud architectures. We will present how to support simultaneous, consistent and high-performance access through multiple industry open source and cloud compatible standards of streaming, table, TSDB, object, and file APIs. A new serverless technology is also used in the architecture to support a dynamic and flexible implementations. The presenter will also outline how the platform was integrated with the Spark eco-system, including AI and ML tools, to simplify the development process

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