Differentiate Big Data vs Data Warehouse use cases for a cloud … · 2019-04-24 · Hadoop and...
Transcript of Differentiate Big Data vs Data Warehouse use cases for a cloud … · 2019-04-24 · Hadoop and...
Differentiate Big Data vs Data Warehouse use cases for a cloud solution
James SerraBig Data Evangelist
Microsoft
Blog: JamesSerra.com
About Me
▪ Microsoft, Big Data Evangelist
▪ In IT for 30 years, worked on many BI and DW projects
▪ Worked as desktop/web/database developer, DBA, BI and DW architect and developer, MDM
architect, PDW/APS developer
▪ Been perm employee, contractor, consultant, business owner
▪ Presenter at PASS Business Analytics Conference, PASS Summit, Enterprise Data World conference
▪ Certifications: MCSE: Data Platform, Business Intelligence; MS: Architecting Microsoft Azure
Solutions, Design and Implement Big Data Analytics Solutions, Design and Implement Cloud Data
Platform Solutions
▪ Blog at JamesSerra.com
▪ Former SQL Server MVP
▪ Author of book “Reporting with Microsoft SQL Server 2012”
I tried to understand the Microsoft data platform on my own…
And felt like I was body slammed by Randy
Savage:
Let’s prevent that from happening…
Agenda
• Data Lake Driven Analytics
• Compute technologies
• Architecture Patterns
Enterprise Data Maturity Stages
Structured data is transacted and locally managed. Data used reactively
STAGE 2: Informative
STAGE 1: Reactive
Structured data is managed and analyzed centrally and informs the business
Data capture is comprehensive and scalable and leads business decisions based on advanced analytics
STAGE 4: Transformative
STAGE 3: Predictive Data transforms
business to drive desired outcomes.
Leaders(Real-time
intelligence)
Laggards(rear-view
mirror)
Any data, any source, anywhere at scale
Benefits of the cloud for Big Data
Agility• Unlimited elastic scale
• Pay for what you need
Innovation• Quick “Time to market”
• Fail fast
Risk• Availability
• Reliability
• Security
Total cost of ownership calculator: https://www.tco.microsoft.com/
Harness the growing and changing nature of data
Need to collect any data
StreamingStructured
Challenge is combining transactional data stored in relational databases with less structured data
Big Data = All Data. Requires a data lake
Get the right information to the right people at the right time in the right format
Semi-structured
“ ”
Observation
Pattern
Theory
Hypothesis
What will happen?
How can we make it happen?
Predictive
Analytics
Prescriptive
Analytics
What happened?
Why did it happen?
Descriptive
Analytics
Diagnostic
Analytics
Confirmation
Theory
Hypothesis
Observation
Two Approaches to getting value out of data: Top-Down + Bottoms-Up
Implement Data Warehouse
Physical Design
ETL
Development
Reporting &
Analytics
Development
Install and Tune
Reporting & Analytics Design
Dimension Modelling
ETL Design
Setup Infrastructure
Understand Corporate Strategy
Data Warehousing Uses A Top-Down Approach
Data sources
Gather Requirements
Business Requirements
Technical Requirements
The “data lake” Uses A Bottoms-Up Approach
Ingest all data regardless of requirements
Store all data in native format without
schema definition
Do analysisUsing analytic engines
like Hadoop
Interactive queries
Batch queries
Machine Learning
Data warehouse
Real-time analytics
Devices
Exactly what is a data lake?
A storage repository, usually Hadoop, that holds a vast amount of raw data in its native format until it is needed.
• Inexpensively store unlimited data
• Collect all data “just in case”
• Store data with no modeling – “Schema on read”
• Complements Enterprise data warehouse (EDW)
• Frees up expensive EDW resources
• Quick user access to data
• ETL Hadoop tools
• Easily scalable
• Place to backup data to
• Place to move older data (archive)
Needs data governance so your data lake does not turn into a data swamp!
Objectives✓ Plan the structure based on optimal data retrieval✓ Avoid a chaotic, unorganized data swamp
Data Retention PolicyTemporary data
Permanent data
Applicable period (ex: project lifetime)
etc…
Business Impact / CriticalityHigh (HBI)
Medium (MBI)
Low (LBI)
etc…
Confidential ClassificationPublic information
Internal use only
Supplier/partner confidential
Personally identifiable information (PII)
Sensitive – financial
Sensitive – intellectual property
etc…
Probability of Data AccessRecent/current data
Historical data
etc…
Owner / Steward / SME
Subject Area
Security BoundariesDepartment
Business unit
etc…
Time PartitioningYear/Month/Day/Hour/Minute
Downstream App/Purpose
Common ways to organize the data:
Data Lake + Data Warehouse Better Together
Data sources
What happened?
Descriptive
Analytics
Diagnostic
Analytics
Why did it happen?
What will happen?
Predictive
Analytics
Prescriptive
Analytics
How can we make it happen?
Data Lake Data Warehouse
Schema-on-read Schema-on-write
Physical collection of uncurated data Data of common meaning
System of Insight: Unknown data to do
experimentation / data discovery
System of Record: Well-understood data to do
operational reporting
Any type of data Limited set of data types (ie. relational)
Skills are limited Skills mostly available
All workloads – batch, interactive, streaming,
machine learning
Optimized for interactive querying
Complementary to DW Can be sourced from Data Lake
Data WarehouseServing, Security & Compliance• Business people
• Low latency
• Complex joins
• Interactive ad-hoc query
• High number of users
• Additional security
• Large support for tools
• Dashboards
• Easily create reports (Self-service BI)
• Know questions
A data lake is just a glorified file folder with data files in it – how many end-
users can accurately create reports from it?
What is Azure Databricks?A fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure
Best of Databricks Best of Microsoft
Designed in collaboration with the founders of Apache Spark
One-click set up; streamlined workflows
Interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.
Native integration with Azure services (Power BI, SQL DW, Cosmos DB, Blob Storage)
Enterprise-grade Azure security (Active Directory integration, compliance, enterprise -grade SLAs)
Azure HDInsight
Hadoop and Spark as a Service on Azure
Fully-managed Hadoop and Spark
for the cloud
100% Open Source Hortonworks
data platform
Clusters up and running in minutes
Managed, monitored and supported
by Microsoft with the industry’s best SLA
Familiar BI tools for analysis, or open source
notebooks for interactive data science
63% lower TCO than deploy your own
Hadoop on-premises*
*IDC study “The Business Value and TCO Advantage of Apache Hadoop in the Cloud with Microsoft Azure HDInsight”
Hortonworks Data Platform (HDP) 3.0
Simply put, Hortonworks ties all the open source products together (20)
(under the covers of HDInsight 4.0 – public preview)
Have a mature Hadoop ecosystem already established that you want to migrate
Want to be 100% open source
Want to use Hadoop tools and make them available 24/7 at a lower cost
Want to use certain tools like Kafka/Storm/HBase/R Server/LLAP/Pig/Beam/Flink
CONTROL EASE OF USE
Azure Data Lake
Analytics
Azure Data Lake Store Gen2
Azure Storage
Any Hadoop technology,
any distribution
Workload optimized,
managed clusters
Azure MarketplaceHDP | CDH | MapR
IaaS Clusters Managed Clusters
Azure HDInsight
Frictionless & Optimized
Spark clusters
Azure Databricks
BIG
DA
TA
S
TO
RA
GE
BIG
DA
TA
A
NA
LYT
ICS
Red
uced
Ad
min
istr
ati
on
K N O W I N G T H E V A R I O U S B I G D A T A S O L U T I O N S
Azure SQL Data WarehouseA relational data warehouse-as-a-service, fully managed by Microsoft.
Industries first elastic cloud data warehouse with enterprise-grade capabilities.
Support your smallest to your largest data storage needs while handling queries up to 100x faster.
SMP vs MPP
Azure Analysis Services
Microsoft Products vs Hadoop/OSS Products
Note: Many of the Hadoop/OSS products are available in Azure
Microsoft Product Hadoop/Open Source Software Product
Office365/Excel OpenOffice/Calc
Cosmos DB MongoDB, MarkLogic, HBase, Cassandra
SQL Database SQLite, MySQL, PostgreSQL, MariaDB, Apache Ignite
Azure Data Lake Analytics/YARN None
Azure VM/IaaS OpenStack
Blob Storage HDFS, Ceph
Azure HBase Apache HBase (Azure HBase is a service wrapped around Apache HBase), Apache Trafodion
Event Hub Apache Kafka
Azure Stream Analytics Apache Storm, Apache Spark Streaming, Apache Flink, Apache Beam, Twitter Heron
Power BI Apache Zeppelin, Apache Jupyter, Airbnb Caravel, Kibana
HDInsight Hortonworks (pay), Cloudera (pay), MapR (pay)
Azure ML (Machine Learning) Apache Mahout, Apache Spark MLib, Apache PredictionIO
Microsoft R Open R
SQL Data Warehouse/Interactive queries Apache Hive LLAP, Presto, Apache Spark SQL, Apache Drill, Apache Impala
IoT Hub Apache NiFi
Azure Data Factory Apache Falcon, Airbnb Airflow, Apache Oozie, Apache Azkaban
Azure Data Lake Storage/WebHDFS HDFS Ozone
Azure Analysis Services/SSAS Apache Kylin, Apache Druid, AtScale (pay)
SQL Server Reporting Services None
Hadoop Indexes Jethro Data (pay)
Azure Data Catalog Apache Atlas
PolyBase Apache Drill
Azure Search Apache Solr, Apache ElasticSearch (Azure Search build on ES)
SQL Server Integration Services (SSIS) Talend Open Studio, Pentaho Data Integration
OthersApache Ambari (manage Hadoop clusters), Apache Ranger (data security such as row/column-level security), Apache Knox (secure entry point for
Hadoop clusters), Apache Flume (collecting log data)
Compute pool
SQL Compute
Node
SQL Compute
Node
SQL Compute
Node…
Compute pool
SQL Compute
Node
IoT data
Directly
read from
HDFS
Persistent storage
…
Storage pool
SQL
ServerSpark
HDFS Data Node
SQL
ServerSpark
HDFS Data Node
SQL
ServerSpark
HDFS Data Node
Kubernetes pod
AnalyticsCustom
apps BI
SQL Server
master instance
Node Node Node Node Node Node Node
SQL
Data mart
SQL Data
Node
SQL Data
Node
Compute pool
SQL Compute
Node
Storage Storage
© Microsoft Corporation
Azure Data ExplorerFast and fully managed big data analytics service
Fully managed
for efficiency
Focus on insights, not the infra-
structure for fast time to value
No infrastructure to manage;
provision the service, choose the
SKU for your workload, and
create database.
Optimized for
streaming data
Get near-instant insights
from fast-flowing data
Scale linearly up to 200 MB per
second per node with highly
performant, low latency ingestion.
Designed for
data exploration
Run ad-hoc queries using the
intuitive query language
Returns results from 1 Billion
records < 1 second without
modifying the data or metadata
© Microsoft Corporation
Azure Data Explorer overview
1. Capability for many data types,
formats, and sources
Structured (numbers), semi-structured
(JSON\XML), and free text
2. Batch or streaming ingestion
Use managed ingestion pipeline or
queue a request for pull ingestion
3. Compute and storage isolation
• Independent scale out / scale in
• Persistent data in Azure Blob Storage
• Caching for low-latency on compute
4. Multiple options to support
data consumption
Use out-of-the box tools and connectors
or use APIs/SDKs for custom solution
Questions to ask client
• Can you use the cloud?
• Is this a new solution or a migration?
• Do the developers have Hadoop skills?
• Will you use non-relational data (variety)?
• How much data do you need to store (volume)?
• Is this an OLTP or OLAP/DW solution?
• Will you have streaming data (velocity)?
• Will you use dashboards?
• How fast do the operational reports need to run?
• Will you do predictive analytics?
• Do you want to use Microsoft tools or open source?
• What are your high availability and/or disaster recovery requirements?
• Do you need to master the data (MDM)?
• Are there any security limitations with storing data in the cloud?
• Does this solution require 24/7 client access?
• How many concurrent users will be accessing the solution at peak-time and on average?
• What is the skill level of the end users?
• What is your budget and timeline?
• Is the source data cloud-born and/or on-prem born?
• How much daily data needs to be imported into the solution?
• What are your current pain points or obstacles (performance, scale, storage, concurrency, query times, etc)?
• Are you ok with using products that are in preview?
Advanced Analytics
Social
LOB
Graph
IoT
Image
CRM
INGEST STORE PREP MODEL & SERVE(& store)
Data orchestration and monitoring
Big data store Transform & Clean Data warehouse
AI
BI + Reporting
Azure Data Factory
SSIS
Azure Data Lake Storage Gen2
Blob Storage
Azure Data Lake Storage Gen1
SQL Server 2019 Big Data Cluster
Azure Databricks
Azure HDInsight
PolyBase & Stored Procedures
Power BI Dataflow
Azure Data Lake Analytics
Azure SQL Data Warehouse
Azure Analysis Services
SQL Database (Single, MI, HyperScale)
SQL Server in a VM
Cosmos DB
Power BI Aggregations
Blob Storage Data Lake Store
Azure Data Lake Storage Gen2
Large partner ecosystem
Global scale – All 50 regions
Durability options
Tiered - Hot/Cool/Archive
Cost Efficient
Built for Hadoop
Hierarchical namespace
ACLs, AAD and RBAC
Performance tuned for big data
Very high scale capacity and throughput
Large partner ecosystem
Global scale – All 50 regions
Durability options
Tiered - Hot/Cool/Archive
Cost Efficient
Built for Hadoop
Hierarchical namespace
ACLs, AAD and RBAC
Performance tuned for big data
Very high scale capacity and throughput
INGEST STORE PREP & TRAIN MODEL & SERVE
C L O U D D A T A W A R E H O U S E
Azure Data Lake Store Gen2
Logs (unstructured)
Azure Data Factory
Microsoft Azure also supports other Big Data services like Azure HDInsight to allow customers to tailor the above architecture to meet their unique needs.
Media (unstructured)
Files (unstructured)
PolyBase
Business/custom apps
(structured)
Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
INGEST STORE PREP & TRAIN MODEL & SERVE
M O D E R N D A T A W A R E H O U S E
Azure Data Lake Store Gen2
Logs (unstructured)
Azure Data Factory
Azure Databricks
Microsoft Azure also supports other Big Data services like Azure HDInsight to allow customers to tailor the above architecture to meet their unique needs.
Media (unstructured)
Files (unstructured)
PolyBase
Business/custom apps
(structured)
Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
A D V A N C E D A N A L Y T I C S O N B I G D A T A
INGEST STORE PREP & TRAIN MODEL & SERVE
Cosmos DB
Business/custom apps
(structured)
Files (unstructured)
Media (unstructured)
Logs (unstructured)
Azure Data Lake Store Gen2Azure Data Factory Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
PolyBase
SparkR
Azure Databricks
Microsoft Azure also supports other Big Data services like Azure HDInsight, Azure Machine Learning to allow customers to tailor the above architecture to meet their unique needs.
Real-time apps
INGEST STORE PREP & TRAIN MODEL & SERVE
R E A L T I M E A N A L Y T I C S
Sensors and IoT
(unstructured)
Apache Kafka for
HDInsight Cosmos DB
Files (unstructured)
Media (unstructured)
Logs (unstructured)
Azure Data Lake Store Gen2Azure Data Factory
Azure Databricks
Real-time apps
Business/custom apps
(structured)
Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
Microsoft Azure also supports other Big Data services like Azure IoT Hub, Azure Event Hubs, Azure Machine Learning to allow customers to tailor the above architecture to meet their unique needs.
PolyBase
Customer
analytics
Financial
modeling
Risk, fraud,
threat detection
Credit
analytics
Marketing
analytics
Faster innovation for a better
customer experience
Improved consumer outcomes and
increased revenue
Enhanced customer experience with
machine learning
Transform growth with predictive
analytics
Improved customer engagement with machine learning
Customer profiles
Credit history
Transactional data
LTV
Loyalty
Customer segmentation
CRM data
Credit data
Market data
CRM
Credit
Risk
Merchant records
Products and services
Clickstream data
Products
Services
Customer service data
Customer 360degree evaluation
Customer segmentation
Reduced customer churn
Underwriting, servicing and delinquency handling
Insights for new products
Commercial/retail banking, securities, trading and investment models
Decision science, simulations and forecasting
Investment recommendations
Real-time anomaly detection
Card monitoring and fraud detection
Security threat identification
Risk aggregation
Enterprise DataHub
Regulatory andcompliance analysis
Credit risk management
Automated credit analytics
Recommendation engine
Predictive analytics and targeted advertising
Fast marketing and multi-channel engagement
Customer sentiment analysis
Transaction data
Demographics
Purchasing history
Trends
Effective customer
engagement
Decision services
management
Risk and revenue
management
Risk and compliance
management
Recommendation
engine
Financial services use cases
https://aka.ms/ADAG
Q & A ?James Serra, Big Data Evangelist
Email me at: [email protected] (email me for a copy of this deck)
Follow me at: @JamesSerra
Link to me at: www.linkedin.com/in/JamesSerra
Visit my blog at: JamesSerra.com (check out the “presentations” tab)
Who manages what?
Infrastructureas a Service
Storage
Servers
Networking
O/S
Middleware
Virtualization
Data
Applications
Runtime
Man
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man
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Platformas a Service
Sca
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Yo
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Storage
Servers
Networking
O/S
Middleware
Virtualization
Applications
Runtime
Data
On PremisesPhysical / Virtual
Yo
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cale
, m
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esi
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t an
d m
an
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Storage
Servers
Networking
O/S
Middleware
Virtualization
Data
Applications
Runtime
Softwareas a Service
Storage
Servers
Networking
O/S
Middleware
Virtualization
Applications
Runtime
Data
Sca
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Windows Azure
Virtual Machines
Windows Azure
Cloud Services
Microsoft data platform solutionsProduct Category Description More Info
SQL Server 2017 RDBMS Earned top spot in Gartner’s Operational Database magic
quadrant. JSON support. Linux support
https://www.microsoft.com/en-us/server-
cloud/products/sql-server-2017/
SQL Database RDBMS/DBaaS Cloud-based service that is provisioned and scaled quickly.
Has built-in high availability and disaster recovery. JSON
support. Managed Instance option soon
https://azure.microsoft.com/en-
us/services/sql-database/
SQL Data Warehouse MPP RDBMS/DBaaS Cloud-based service that handles relational big data.
Provision and scale quickly. Can pause service to reduce
cost
https://azure.microsoft.com/en-
us/services/sql-data-warehouse/
Azure Data Lake Store Hadoop storage Removes the complexities of ingesting and storing all of
your data while making it faster to get up and running with
batch, streaming, and interactive analytics
https://azure.microsoft.com/en-
us/services/data-lake-store/
HDInsight PaaS Hadoop
compute/Hadoop
clusters-as-a-service
A managed Apache Hadoop, Spark, R Server, HBase, Kafka,
Interactive Query (Hive LLAP) and Storm cloud service made
easy
https://azure.microsoft.com/en-
us/services/hdinsight/
Azure Databricks PaaS Spark clusters A fast, easy, and collaborative Apache Spark based analytics
platform optimized for Azure
https://databricks.com/azure
Azure Data Lake Analytics On-demand analytics job
service/Big Data-as-a-
service
Cloud-based service that dynamically provisions resources
so you can run queries on exabytes of data. Includes U-SQL,
a new big data query language
https://azure.microsoft.com/en-
us/services/data-lake-analytics/
Azure Cosmos DB PaaS NoSQL: Key-value,
Column-family,
Document, Graph
Globally distributed, massively scalable, multi-model, multi-
API, low latency data service – which can be used as an
operational database or a hot data lake
https://azure.microsoft.com/en-
us/services/cosmos-db/
Azure Database for PostgreSQL,
MySQL, and MariaDB
RDBMS/DBaaS A fully managed database service for app developers https://azure.microsoft.com/en-
us/services/postgresql
ExpressRoute
We guarantee 99.95% ExpressRoute dedicated circuit availability
(3.6TB/hour)
(18TB/hour)
(36TB/hour)
(7.2TB/hour)
Data VolumeLo
wH
igh
Latency
Cost/GBHig
h Lo
w
Request Rate
Str
uct
ure
High
Low
SQL
(SQL DB, SQL DW)
NoSQL
(DocDB)
HDFS
(HDP, Cloudera)
ADLS
Hot Data Cold Data
Cache
Data Lake Store: Technical Requirements
64
Secure Must be highly secure to prevent unauthorized access (especially as all data is in one place).
Native format Must permit data to be stored in its ‘native format’ to track lineage & for data provenance.
Low latency Must have low latency for high-frequency operations.
Must support multiple analytic frameworks—Batch, Real-time, Streaming, ML etc.
No one analytic framework can work for all data and all types of analysis.
Multiple analytic
frameworks
Details Must be able to store data with all details; aggregation may lead to loss of details.
Throughput Must have high throughput for massively parallel processing via frameworks such as Hadoop and Spark
Reliable Must be highly available and reliable (no permanent loss of data).
Scalable Must be highly scalable. When storing all data indefinitely, data volumes can quickly add up
All sources Must be able ingest data from a variety of sources-LOB/ERP, Logs, Devices, Social NWs etc.
50 TB
100 TB
500 TB
10 TB
5 PB
1.000
100
10.000
3-5 Way
Joins
▪ Joins +
▪ OLAP operations +
▪ Aggregation +
▪ Complex “Where”
constraints +
▪ Views
▪ Parallelism
5-10 Way
Joins
Normalized
Multiple, Integrated
Stars and Normalized
Simple
Star
Multiple,
Integrated
Stars
TB’s
MB’s
GB’s
Batch Reporting,
Repetitive Queries
Ad Hoc Queries
Data Analysis/Mining
Near Real Time
Data FeedsDaily
Load
Weekly
Load
Strategic, Tactical
Strategic
Strategic, Tactical
Loads
Strategic, Tactical
Loads, SLA
“Query Freedom“
“Query complexity““Data
Freshness”
“Query Data Volume“
“Query Concurrency“
“Mixed
Workload”
“Schema Sophistication“
“Data Volume”
DW SCALABILITY SPIDER CHART
MPP – Multidimensional
Scalability
SMP – Tunable in one dimension
on cost of other dimensions
The spiderweb depicts important attributes to consider when evaluating Data Warehousing options.
Big Data support is newest dimension.
HiveQL T-SQL
Updates INSERT UPDATE, INSERT, DELETE
Transactions Supported Supported
Index Types Compact, Bitmap Clustered, Non-Clustered, Columnar
Latency Minutes Seconds
Data Types + map, struct, array Int, float, boolean, char, binary..
Functions Dozens of built-in functions Hundreds of built-in functions
Multi-Table Inserts Supported Not Supported
Partitioning Supported Supported
Multi-Level Partitioning Supported Not Supported
Views Read-Only Read-Only and Updateable
Extensibility UDF’s, MapReduce Scripts UDF’s, Stored Procedures
HDInsight vs HDP on Azure VM
HDInsight HDP on Azure VM
PaaS (setup, scale, manage, patch, etc) IaaS
Managed by Microsoft Managed by customer
Storage separate (Blob or ADLS) Storage in VM (local disk), but can also
have storage in Azure blob or ADLS
Delete VM keeps data Delete VM deletes data (unless external)
Up to 30-days behind latest HDP version Latest HDP Version
Limited Hadoop projects Unlimited Hadoop projects
Microsoft supports VM and Hadoop Microsoft: VM, HDP: Hadoop
No on-prem version On-prem version
PolyBase use cases
Ingestion Batch Presentation
Company C
Event hubs
Machine Learning
Flatten & Metadata Join
Data Factory: Move Data, Orchestrate, Schedule, and Monitor
Machine Learning Azure SQL
Data Warehouse
Power BI
INGEST PREPARE ANALYZE PUBLISH
ASA Job Rule #2
CONSUMEDATA SOURCES
Cortana
Web/LOB Dashboards
On Premise
Hot Path
Cold Path
Archived Data
Data Lake Store
Simulated Sensors and devices
Blobs –Reference Data
Event hubs ASA Job Rule #1
Event hubs
Real-time Scoring
Aggregated Data
Data Lake Store
CSV Data
Data Lake Store
Data Lake Analytics
Batch Scoring
Offline Training
Hourly, Daily, Monthly Roll Ups
Ingestion
Batch
PresentationSpeed