Oracle Week 2016 - Modern Data Architecture

Modern Operational Data Architecture

Arthur Gimpel, DataZone

About Me

• Name: Arthur Gimpel

• Position: Technology Evangelist, Solutions Architect, Trainer

• Tech Stack: MongoDB, SQL Server, Couchbase, Elastic Stack, Redis, Kafka, Python, .NET

Relational Databases

• First RDBMS was introduced in late 1970s • Exist in all possible flavors but share one

thing - ACID • Still dominate the database market

RDBMS In Theory

• Atomicity: All or nothing approach, transactions

• Consistency: Hard state, every transaction changes the whole DBMS

• Isolation: Transactions cannot interfere with each other

• Durability: Every transaction is persisted

RDBMS Is Not Perfect

• Everything is persisted, synchronously. Limited by IO performance

• All data is bound to a tabular schema, hard to make changes in big databases

• ACID makes horizontal scaling nearly* impossible

• Complex schema slows down aggregations and queries drastically

NoSQL

• Distributed / Horizontal Scalability • Mostly Open Source • Mostly schema less:

• Key - Value • Document • Graph

• Serves specific purposes

NoSQL - Key Value Stores

• Key: • Usually string, equivalent to primary key in a

relational database

• Value: • Simple values: Int, Float, DateTime • Complex values: Array, Binary, XML, JSON

Key Value - Characteristics• Database is usually a set of unique keys,

and its values • KV data stores are usually easy to

distribute • Key Value access usually is VERY fast • Indexing and querying values is usually

challenging

Key Value - Use Cases• Distributed caching

• Session / temporary user data

• Ad tech: Impressions

• Ad tech: Serving data - profiles, segments

• Recommendation engines - main data store

NoSQL - Graph Stores“In computing, a graph database is a database

that uses graph structures for semantic

queries with nodes, edges and properties to

represent and store data” (Wikipedia)

Graph - Characteristics• Nodes are entities - for example a person

• Properties describe nodes - for example age, name

• Edges are relations between nodes and/or properties

Graph - Use Cases• Fraud detection

• Recommendation engines - link analysis

• Intelligence systems

• Social Networks

• Medical Research

NoSQL - Document Stores

• Document databases usually store JSON • Used to store object oriented data • Usually used to avoid relational - object

mismatch • Document stores have the highest

adoption rate among NoSQL databases

Document Store - Characteristics• Information is stored in JSON variations

• Some document stores support secondary indexes for easier querying

• Documents are usually divided to logical groups (collections, buckets, types - instead of RDBMS tables)

Document Store - Use Cases• “Relational” use cases where there is a

need for high scale (volume, velocity, variety)

• Hierarchal data - aggregations

• Search use cases

NoSQL - Challenges

• Every data store has its purpose. There is no single solution to all database needs

• NoSQL does not implement all of RDBMS’s abilities (CDC, Jobs, Stored Procedures, Triggers)

• Every data store has its own languages, and APIs. There is no ANSI SQL

Not Only SQL

Polyglot Persistence Sample Use Cases

• Add search capabilities to your database

• Split session / temporary data processing to key value stores

• Add Graph analysis capabilities to your operational database

Search Use Case

Search: Architecture #1

Search: Architecture #2

Architecture Comparison

Architecture #1 Architecture #2

Data distribution strategy Data store based Application based

Data distribution component Data Pipeline Message Queue

Implementation Team Data Engineers / DevOps DevOps / Developers

Implementation Complexity

Low: Data pipeline development

High: data access layer refactor

Scalability Limited to RDBMS ScaleFully scalable regardless

of RDBMS

Summary

• Chose the relevant database engine for the right mission - replacing databases is not easy

• Do not hesitate to use more than one database engine in your operational application, single point of truth will be created in the analytical stack

• Sizing is no replacement for benchmark. Check your deployment carefully

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