Google Analytics & Big Data

What if we applied some of the most sophisticated, general-purpose data analytics tools, which are typically used by corporate, big data analysts, to our GA data? Could these tools help us unlock some insights that are hard to find using Google’s standard interface?

In this talk, Aaron, VP of Global Community at MapD, will start by talking about some of the most frustrating, hard to find, or just plain missing pieces of data in the Google Analytics web interface. Then, using MapD’s own web data, he’ll show how to import GA data into a big data analytics tool, create simple dashboards that fill in the gaps of GA’s web interface, and turn that GA data into something that is actionable and even predictive.

Transcript

1. Big Data + Google Analytics Aaron Williams @_arw_ November 8, 2017
2. “Every business will become a software business, build applications, use advanced analytics and provide Saas services.” - Smart CEO Guy has
3. The Evolution of Data as a Weapon 3 Collect It Make It Actionable Make it Predictive
4. GPUs Recharge Moore’s Law 4 GPU Processing Power 50% per year Data Growth 40% per year CPU Processing Power 20% per year
5. Core Density Makes a Huge Difference 5 GPU Processing CPU Processing 40,000 Cores 20 Cores *fictitious example Latency Throughput CPU 1 ns per task (1 task/ns) x (20 cores) = 20 tasks/ns GPU 10 ns per task (0.1 task per ns) x (40,000 cores) = 4,000 task per ns Latency: Time to do a task. | Throughput: Number of tasks per unit time.
6. MapD: Extreme Analytics 6 100x Faster Queries MapD Core The world’s fastest columnar database, powered by GPUs + Visualization at the Speed of Thought MapD Immerse A visualization front end that leverages the speed & rendering superiority of GPUs
7. MapD System Architecture Accelerating the existing data infrastructure 7
8. 8 MAPD DEMO
9. Query Compilation with LLVM 9 Traditional DBs can be highly inefficient • each operator in SQL treated as a separate function • incurs tremendous overhead and prevents vectorization MapD compiles queries w/LLVM to create one custom function • Queries run at speeds approaching hand-written functions • LLVM enables generic targeting of different architectures (GPUs, X86, ARM, etc). • Code can be generated to run query on CPU and GPU simultaneously 10111010101001010110101101010101 00110101101101010101010101011101 LLVM
10. Keeping Data Close to Compute MapD maximizes performance by optimizing memory use 10 SSD or NVRAM STORAGE (L3) 250GB to 20TB 1-2 GB/sec CPU RAM (L2) 32GB to 3TB 70-120 GB/sec GPU RAM (L1) 24GB to 256GB 1000-6000 GB/sec Hot Data Speedup = 1500x to 5000x Over Cold Data Warm Data Speedup = 35x to 120x Over Cold Data Cold Data COMPUTE LAYER STORAGE LAYER Data Lake/Data Warehouse/System Of Record Speed Increases Space Increases
11. MapD Immerse Using a hybrid approach to speed and scale visualization 11 Basic charts are frontend rendered using D3 and other related toolkits Scatterplots, pointmaps + polygons are backend rendered using the Iris Rendering Engine on GPUs Geo-Viz is composited over a frontend rendered basemap
12. MapD Benchmarks Blogger Mark Litwintschik benchmarked MapD on a billion-row taxi data set and found it to be up to orders-of-magnitude faster than the fastest CPU databases 12 MapD Core: Comparative Query Acceleration* System Q 1 Q 2 Q 3 Q 4 BrytlytDB & 2-node p2.16xlarge cluster 36x 47x 25x 12x ClickHouse, Intel Core i5 4670K 49x 58x 32x 25x Redshift, 6-node ds2.8xlarge cluster 74x 24x 14x 6x BigQuery 95x 38x 6x 6x Presto, 50-node n1-standard-4 cluster 190x 75x 61x 41x Amazon Athena 305x 117x 37x 13x Elasticsearch (heavily tuned) 386x 343x n/a n/a Spark 2.1, 11 x m3.xlarge cluster w/ HDFS 485x 153x 119x 169x Presto, 10-node n1-standard-4 cluster 524x 189x 127x 61x Vertica, Intel Core i5 4670K 685x 607x 203x 132x Elasticsearch (lightly tuned) 1,642x 1,194x n/a n/a Presto, 5-node m3.xlarge cluster w/ HDFS 1,667x 735x 388x 159x Presto, 50-node m3.xlarge cluster w/ S3 2,048x 849x 164x 86x PostgreSQL 9.5 & cstore_fdw 7,238x 3,302x 1,424x 722x Spark 1.6, 5-node m3.xlarge cluster w/ S3 12,571x 5,906x 3,758x 1,884x *All speed comparisons are to the “MapD & 1 Nvidia Pascal DGX-1” benchmark Source: http://tech.marksblogg.com/benchmarks.html
13. Try MapD It’s free and it’s easy 13 Play with the live demos: https://www.mapd.com/demos/ Download the Community Edition: https://www.mapd.com/platform/download-community/ Join our forums: https://community.mapd.com/ Review these slides: https://speakerdeck.com/mapd
14. AWS Credits Available 14 Free GPU Compute! We’re looking for interesting Google Analytics use cases. Email Aaron Williams (aaron@mapd.com) with your ideas!
15. Aaron Williams VP of Global Community @_arw_ aaron@mapd.com /in/aaronwilliams/ /williamsaaron