Real Use Cases - Pentaho & Big Data Ecosystem

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Xpand IT

Presentation to demonstrate a set of inspiring use cases. By Ricardo Pires – Partner & BI Lead @XpandIT

Technology

Ricardo Pires – Partner & BI Lead Xpand IT
Real Uses Cases

USE CASE 1:
ALL TRANSACTIONS,
ONE DASHBOARD

• 3 Years historical data
• 7,2 billion transactions representing 4,5TB
• Wide group of users spread across the organization
• Intuitive User Interface with a great User Experience
• Detailed visualization
• Row level security
• Maximum dashboard load time 5s
CHALLENGES

THE SOLUTION
HDFS Hive Impala
Pentaho Data Integration (PDI)
PDI
HBase
Web
Application
Hadoop

• Impala on Cloudera Hadoop can be used as an interactive data
base
• Hadoop distributed nature allows implementing used cases that
wouldn’t be viable on other technologies
• We went from 7 days of data to 3 years
• Pentaho Data Integration implements and orchestrates the whole
ETL process, making it much easier
• From traditional data sources to summarized data on Hadoop
KEY TAKEAWAYS

• Data lake goal is to make data available on a centralized location
• Requires dealing with
• Wide set of sources
• Disparate technologies
• In this case it is a repetitive batch loading process
DATA INGESTION

THE SMART SOLUTION
Configure
Metadata
Repository
Ingestion Engine
based on
Templates
Use Hadoop as
Data Repository
METADATA DRIVEN INGESTION

ARCHITECTURE
HDFS
Web UI
HadoopAny Datasource
PDI
PDI
PDI
{REST}
Ingestion Engine
Hive

• Pentaho Data Integration flexibility is a great match for Hadoop
semi-structured nature
• Cloudera Hadoop can be easily used to store data and make it
immediately available through a SQL interface
• Patterns and well defined workflows are essential to data
governance
KEY TAKEAWAYS

• Government agencies have long collected data but that doesn’t
mean it can easily be perceived by citizens
• Challenge
• Create an intuitive UI to represent more than 100 KPIs across 308
municipalities
• Become a standard in terms of transparency
GOVERNMENT CHALLENGE

Architecture
BA
SERVER
Public Data
Service
PDI
ETL Web Application

• Pentaho Business Analytics is a comprehensive suite
• Pentaho Server components are really flexible and extensible
allowing creating custom UIs such as:
• Analytics portals
• Embed on existing products
KEY TAKEAWAYS

Hive tables are an integral part of the big data ecosystem, but the simple directory-based design that made them ubiquitous is increasingly problematic. Netflix uses tables backed by S3 that, like other object stores, don’t fit this directory-based model: listings are much slower, renames are not atomic, and results are eventually consistent. Even tables in HDFS are problematic at scale, and reliable query behavior requires readers to acquire locks and wait. Owen O’Malley and Ryan Blue offer an overview of Iceberg, a new open source project that defines a new table layout addresses the challenges of current Hive tables, with properties specifically designed for cloud object stores, such as S3. Iceberg is an Apache-licensed open source project. It specifies the portable table format and standardizes many important features, including: * All reads use snapshot isolation without locking. * No directory listings are required for query planning. * Files can be added, removed, or replaced atomically. * Full schema evolution supports changes in the table over time. * Partitioning evolution enables changes to the physical layout without breaking existing queries. * Data files are stored as Avro, ORC, or Parquet. * Support for Spark, Pig, and Presto.

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