Big Data Analytics Nokia
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1 Big Data Analytics Nokia 1 Selecting the Right Tool for the Right Workload Yekesa Kosuru Nokia Location & Commerce Strata + Hadoop World NY - Oct 25, 2012
2 Agenda Big Data Analytics Who we are Use case data flows Big data platform Big data challenges Selecting the Right Tool for the Right Workload Hadoop VS SQL Which analytical database Why InfiniDB 2
3 Great Mobile Products That Sense the World CREATE A LEADING WHERE PLATFORM WIN IN SMART DEVICES CONNECT THE NEXT BILLION INVEST IN FUTURE DISRUPTIONS 3 Nokia Internal Use Only
4 One Platform, Enabling Contextually Rich Mobile Experiences Content Platform Maps Positions Places Directions Guidance Traffic Smart Data Apps 4 Nokia Internal Use Only
5 Click to edit Master title style Big DATA ANALYTICS 5
6 Business Challenges Data silos, missing semantics Multiple sources - overlapping, conflicting Timely processing of large volumes of data Partial, insufficient, inaccurate, inconsistent.. data Security, privacy and other policies unknown Central Analytics Platform created! 6
7 Statistics 10 s PB of data all across Nokia Multi-tenant, multi-petabyte analytics cluster 10-20K+ jobs per day 600+ internal users 250M+ KV queries Over a terabyte flowing every day Multiple data centers around the world 7
8 Places Data Store (POI)- Use Case Authentication Account Management Access Control Places Manager FTP Data Intake Oozie Sched Data Processing Hive BI Pig Places Content Analytics Places Extract Portal Search Suppliers HDFS MR Blend Platform Cloud Infrastructure MR Analyti cal DB SQL Places API Transaction al Data K-V Store Data flow 1 User Logs In 2 Place CRUD Updated Supplier ETL and Blend Places Data Blend Uploads Data places Analytics Record 7 Delivered to OnlineSystems 8 Nokia Internal Use Only
9 Big Data Analytics Platform Data Flows Data Discovery Web Applications Activity Logs VShards (NoSQL) Data Asset Catalog Device Applications Device Activity Sensor Data Intake ETL, Algorithms Aggregation Analytical DB InfiniDB Reports Reference Data 3rd Party User Profile HDFS Oracle Dashboard s Probes POI, Map Batch Queries Analytics Cluster Interactive Queries 9 Nokia Internal Use Only
10 Big Data Analytics Platform Data Flows Data Discovery Data Asset Catalog Data Intake ETL, Algorithms Aggregation Analytical DB InfiniDB HDFS Oracle Batch Queries Interactive Queries Analytics Cluster 10 Nokia Internal Use Only
11 Big Data Analytics Platform Logical Tiers Technology Platform Data Platform End User Layer (not shown) ETL, Algorithms Aggregation Data Asset Catalog Data Data Intake HDFS Analytical DB Technology
12 Technology Platform Hadoop R VShards (KV) Scribe, FTP Hive, Pig InfiniDB, Oracle Export/ Import Workflow Engine Config./ Deploy Monitor Alerts Archiver Scheduler Security/Kerberos & ACL Cloud Infrastructure 12
13 Data Platform Workflow Orchestration Self Serve Tools ETL, Agg Algorithms Data Quality Data Asset Catalog Data, Metadata, Operational Data Technology Platform 13
14 Data Platform Analytics Lifecycle Collect Ingest Organize Analyze Deliver Self Serve Tools ETL, Agg Algorithms Data Quality Data Asset Catalog Data, Metadata, Operational Data Technology Platform 14
15 Nokia Internal Use Only Data Platform: Managing the Data Asset Data Quality - garbage in, garbage out Rules for validating, cleaning data, other heuristics Trusting your insights Process Quality Light weight governance (semantics, integrity, privacy and quality) Data Asset Catalog describe your data Capture essential metadata and logical domain models for assets physical model, logical model, policies, classifications dependencies with other assets Serves as a entry-point to data browsing and asset discovery Insulates subject matter experts from physical details of data asset
16 Nokia Internal Use Only Big Data Challenges At every level - capture, curate, storage, process, visualize.. Hadoop or SQL? Performance of analytical database? Batch or Interactive analysis Neither SQL nor MR fits all problems Data & Metadata Fragmentation
17 Click to edit Master title style Selecting the Right Tool for the Right Workload 17
18 Hadoop VS SQL/Analytical DB SQL/DW SQL/Analytical DB Discover the question Standard industry tools Interactive/Fast No coding Interactive/Fast (secs) Standard industry tools Mutable (Type 1 SCD) No coding, e.g. builtin functions Schema on Write Analyst Reasonable complex Time to Wisdom Discover the question Hadoop/Hive/MR ETL on steroids, Scale Batch/slow Bunch of coding, arbitrary complex Harvest & load into DW Discover the answer 18 Nokia Internal Use Only
19 Why InfiniDB? Cloud deployment model Column oriented, MPP, clean architecture Horizontal and vertical partitioning, clever pruning No indexes Efficient joins Impressive benchmarks Stream based MR like processing Works with BI tools (standard JDBC driver) 19
20 InfiniDB vs Hive Performance Execution Time(secs) Query InfiniDB (sec) Hive (sec) A B C D E F G infinidb (sec) Hive (sec) 0 A B C D E F G Analytic Queries Nokia Internal Use Only
21 21 Click to edit Master title style InfiniDB Under the Hood
22 What is InfiniDB? Scalable Fast Simple 22
23 Analytics Data Platform Foundation Analytics Data Platform Columnar Performance Efficiency MapReduce style Query Execution Widely used MySQL Interface 23
24 InfiniDB Building Blocks or Single Server Purpose built for big data analytics. User Module (UM) Performance Module (PM) 24
25 InfiniDB Building Blocks or Single Server Purpose built for big data analytics. User Module (UM) Understands SQL Performance Module (PM) Operates on data blocks 25
26 InfiniDB M/R Style Distribution of Work Map-Reduce Inside InfiniDB DoW Scalability Linear Linear N-squared Problem Avoided Avoided Hadoop M/R Latency Low Medium-High Intermediate Results Handling Stream-based File-based Report Language SQL Erlang M/R, Hive, Pig Tuning Automatic Manual Real-Time Analytics Real-time access to granular data Ad-Hoc Full Ad-Hoc performance None Access to pre-defined aggregates Data Storage Structured Unstructured 26 Nokia Internal Use Only
27 Independent InfiniDB Benchmark Q4 Series 5 table Joins Q1 Series 2 table Joins Q2 Series 3 table Joins Q3 Series 4 table Joins 27
28 Takeaways Hadoop is good but. Pay attention to data quality Hadoop or SQL Describe your data 28
29 THANK YOU Yekesa Kosuru Distinguished Architect, Nokia Jim Tommaney CTO,
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