Best Practices for Developing Real-Time Dashboards презентация

Содержание

Additional Contributors Abhishek Lal Dan Rosanova David Liu Shane Zhang Santosh Balasubramanian Josh Caplan Kasper de Songe Simon Lidberg Benjamin Wright-Jones Brent Stineman Dan Manrique

Слайд 1 Best Practices for Developing Real-Time Dashboards
Jen Underwood
Microsoft Sr. Program Manager
www.jenunderwood.com @idigdata



Слайд 2Additional Contributors
Abhishek Lal
Dan Rosanova
David Liu
Shane Zhang
Santosh Balasubramanian
Josh Caplan
Kasper de Songe
Simon

Lidberg
Benjamin Wright-Jones
Brent Stineman
Dan Manrique

Слайд 3Real-world, real-time dashboard and alerting use cases
Challenges
Design patterns
Best practices
Build a

real-rime dashboard using REST API
Lessons learned

Best Practices for Developing Real-Time Dashboards

Слайд 4Real-Time Analytics in the Real World

Слайд 5Transformative opportunity
1. IDC. 2. Josh Waldo Senior Director, Cloud Partner Strategy,

Microsoft. 3. Bain & Company, The Value of Big Data: How Analytics Differentiates Winners, 2013.

Слайд 6The ROI of Data
Source: IDC Study: Realizing the Data Dividend, 2014.
The

formula
[data + analytics + people ]
@
speed

Key Opportunity Areas
Organizations can realize Return on Data in several key areas…

Productivity
Includes strategic planning, human capital management, IT optimization

Operations
Includes demand and supply chain management, logistics

Return on Data

$674 billion

Return on Data


$486billion

Return on Data

$158
billion

$235
billion

$1.6 trillion Return on Data

Return on Data

Customer Facing
Includes customer acquisition, retention, support and pricing

Innovations
Includes service, research and development innovation

Слайд 7Real-Time: all processing (from event reception to system response) executes within

seconds, milliseconds or nanoseconds
Near Real-Time: one minute to several minutes
Right-Time: available when needed
On Demand: push or pull data movement unscheduled refreshes of reports

Flavors of Real-Time Analytics

Слайд 8Where is Real-Time Analytics Today
Smart grid
CRM alerting sales with customer scenario
Data

and identity protection services

Sales tracking

Fraud detection

Click-stream analysis

Financial portfolio alerts

Connected car scenario

Слайд 9
Who is using Real-Time Analytics
Source: http://tdwi.org/research/2014/09/best-practices-report-real-time-data-bi-and-analytics/asset.aspx?tc=assetpg

Слайд 11The world is inundated with data, but can marketing access the

data they need? Can they use it effectively?

Right message, right customer, right moment

Marketing

Слайд 12Real-Time Social Analytics SignalR
http://tweetdashboard.azurewebsites.net/

Слайд 13With more pressure than ever on financial leaders, you need to

control costs, reduce risk, monitor financial performance, and quickly assess the impact of business decisions.

The need for speed & transparency

Finance

Слайд 14Your sales team needs a competitive edge
You have lots of data

about customers and prospects. What if you could easily combine it with external data to gain a viewpoint your competitors don’t have?

Sales

Слайд 15Sports ….
Digital data delivery play-by-play game tracking, drive summaries and game

stats for teams and players.

Sports

Source: http://www.forbes.com/sites/jasonbelzer/2015/01/30/ super-bowl-xlix-ushering-in-the-next-age-of-sports-analytics-thanks-to-numberfire/

Слайд 16Botnet architecture is processing 200M+ transactions per day and growing
566,773,255 calls

a day into the sinkhole from over 25 million distinct IP address

Fight against cybercrime

IoT

Слайд 17


Trustworthy Computing
Public Sector
Digital Crimes Unit

Government Affairs

Microsoft Consulting Services
Cybercrime Center
Proactive Disruption
We work

with our customers and partners to proactively target online criminals

MALICIOUS
SOFTWARE CRIMES

Viruses
Trojans

IP CRIMES

Counterfeiting
End-User Piracy

CHILD EXPLOITATION

Child Abuse Images
Trafficking

Worms
Botnets

Security, Reliability, Privacy

Solutions, Initiatives, Innovations

Policy, Legislation

Risk Assessment, Cybersecurity Services

Microsoft

A new era in the fight against cybercrime

Слайд 18







Partners / Subscribers


Botnet Analytics – Architecture
Processing 200M+ transactions per day and

growing
566,773,255 calls a day from over 25 million distinct IP address
Query results returned in under 2 seconds

[Sinkhole]
SQL Server

Internet with
billions of devices

Microsoft
Analytics Platform System

Visualizations
& insights

Certs

ISPs

Others



Excel & Power BI

Azure HDInsight

Azure
Machine Learning



Internet with
billions of devices

Слайд 19Real-Time Analytics at Microsoft Microsoft Xbox team enhances game for HALO

customers

The situation
The HALO contest promotion team wanted to use data to create a better gamer experience for customers playing the new HALO 4.

The solution
Non-technical employees processed & analyzed raw data in real time to answer their questions, using familiar and powerful tools

The benefits
The team quickly uncovered and addressed the unexpected (cheating), while also using insights to update and improve gamer experience.

The process
Analyze raw data
?
Identify trends
?
Make updates
?
Improve CSAT

The tools

Слайд 20Challenges

Слайд 21
Most Common Barriers
Source: http://tdwi.org/research/2014/09/best-practices-report-real-time-data-bi-and-analytics/asset.aspx?tc=assetpg

Слайд 22What do you need to handle this?
An ingestor service that can


Support variety (> million concurrent devices)
Support velocity (> million events/s)
Support volume (> 100s of TB)
with
Buffering to handle variability
Durability
Low latency
Security
and is affordable!

Слайд 23Variable rates
Predictable and unpredictable bursts
Event volume
t

Inactivity
Period
On and off
On and off

workloads (e.g. batch job)
Over provisioned capacity is wasted.

t

Bursts
Peaks due to periodic increased demand
IT complexity and wasted capacity.

Halo 4 game data (1 week starting Monday PST)

Слайд 24Big Data Analytics ≠ Traditional BI with More Data
Big Data is

redefining the processes of managing master data, data quality, and information lifecycle management
Big Data is NOT replacing EDW and OLAP, it supplements those investments
Big Data ecosystem includes variety of analytic technologies
Columnar databases, JSON, and unstructured file stores
Hadoop and NoSQL platforms adding SQL, search, and streaming capabilities, while NoSQL platforms are adding MPP and transactional support
Data tiering that aggressively leverages SSD (Flash) and DRAM

Source: Gartner


Слайд 25Design Patterns

Слайд 26Key technologies in use today

Data federation, replication, data sync, message

buses, and micro batches
Functions designed for high performance include change data capture, columnar databases, in-memory processing, and solid state drives
True real-time using complex event processing (CEP) and continuous stream mining

Enabling Real-Time Analytics

Слайд 27Evolving Approaches to Analytics

ETL Tool
(SSIS, etc)

EDW
(SQL Svr, Teradata, etc)
Extract
Original Data
Load
Transformed Data
Transform
OLTP


ERP


LOB


…

BI

Tools

Data Marts

Data Lake(s)






Dashboards

Apps

Слайд 28Changes in Data Warehousing Patterns
Big Data storage aka Data Lake is

characterized by three key attributes:

Collect everything A data lake contains all data, both raw sources over extended periods of time as well as any processed data
Dive in anywhere A data lake enables users across multiple business units to refine, explore and enrich data on their terms
Flexible access A data lake enables multiple data access patterns across a shared infrastructure: batch, interactive, online, search, in-memory and other processing engine


Слайд 29Changes in Data Warehousing Patterns
Modern MPP, Columnar and Visual Analytics Innovations:

Nature

of Hadoop data access Historically querying Hadoop entailed complex Java, results were slow and batch processes thus improved tools made to expedite Hadoop data access
External tables, compression, HDFS, Hive, other means Easy visual analytics tools use business user friendly means to access Hadoop data and often brings that data into an in-memory cache for rapid data analysis
Materialized Views “v2” and analytic functions Big data visual analytic tools improve upon traditional view techniques to bring bid data into memory or chip and intelligently, automatically re-use and refresh those views

Слайд 30
Hadoop: Move Compute to the Data
Inspired by Google’s Map Reduce
Infrastructure

to automatically scale-out storage and distributed data processing on commodity hardware
Hadoop system components
HDFS – Distributed file system
MapReduce – Programming model
Others: HBase, R, Pig, Hive, Flume, Mahout, Avro, Zookeeper

Слайд 31Hadoop: Move Compute to the Data
Source: Datameer
Another way to think about

this shift…

Слайд 32Source: Tom White’s Hadoop: The Definitive Guide
Hadoop: Move Compute to the

Data

Слайд 33Shift from Schema First to Schema Later

Data arrives
Derive schema
Cleanse data
Transform
Load

to EDW
Analyze



Data arrives
Load to Hadoop
Analyze
Subsets of data loaded to EDW

SLOW VALUE FROM DATA

RAPID VALUE FROM DATA

Слайд 34Changes in Data Warehousing Patterns
Results

Relational and OLAP data


Traditional schema-based data warehouse

applications

EDW







HDFS bridge


Enhanced query engine



External data
source

External file
format

Regular T-SQL

Basically adding a “bridge” to Big Data

Слайд 35Event Hub : Data Ingestion
- Hyper Scale -
- Fully Managed -
-

Interoperable -
- Secure -
- Cost Effective -

Слайд 36
What: Canonical Architecture

Слайд 37
Presentation and action
Transformation
Broker
How: Canonical Architecture
Long term storage
Collection
Producers
Field gateway (Raspberry PI/ Galileo)
(Embedded .Net/
ThreadX)


Event

Hub

Legacy IOT (Custom protocols)

Custom Cloud gateway

AMQP/HTTP
C, .NET, Java…

MQTT/COAP/Custom

Time based data retention,
Consumer groups,
Partitioned receivers,
Check-pointing,
Partition distribution APIs

Low end-to-end latency
Easy integration with analytics frameworks and services

Custom code (Storm/Orleans)

Storage adapters

Configurable adapters for storage devices

AMQP/HTTP, C, .NET, Java…

Scale on throughput (GB/s) and concurrent senders (1M with auth./flow control)


Слайд 38Commonly Used Technologies

Слайд 39Use real-time operational data stores (RT ODSs)
Use real-time data warehouses
Implement changed

data capture (CDC)
Present real-time data and historic data side by side
Define acceptable thresholds and business rules for all entities tracked in real-time

Best Practices for Real-Time Data

Слайд 40Continuous data stream mining
Managing a stream as if it were a

queue
Time windows
Hadoop and Lambda data architecture
Enrich streaming data with other enterprise data
Store stream data to build up a useful history

Best Practices for Streaming Data

Слайд 41Lamda Architecture

Слайд 42Analytics Data Pipeline
BI Tools
Data Marts
Data Lake(s)




Dashboards
Apps
Data Hub
(Storage & Compute)
Data Sources
(Import From)
Data

Connector:
Import from source to Hub

Data Connector:
Import/Export among Hubs

Data Hub
(Storage & Compute)

Data Sources
(Import From)

Data Connector:
Import from source to Hub

Data Connector:
Export from Hub to data store

Pipeline

Pipeline

Transform & Enrich

Collect & Prepare

Publish

Information Production:

Move

Слайд 43Data Pipeline at Microsoft
Azure Service Bus
Relay
Queue
Topic
Notification Hub
Event Hub



NAT and Firewall Traversal

Service Request/Response Services Unbuffered with TCP Throttling



Transactional Cloud AMQP/HTTP Broker High-Scale, High-Reliability Messaging Sessions, Scheduled Delivery, etc.

Transactional Message Distribution
Up to 2000 subscriptions per Topic
Up to 2K/100K filter rules per subscription

High-scale notification distribution
Most mobile push notification services
Millions of notification targets

Слайд 44Building the Dashboard

Слайд 45Temporal query functions
Tumbling Windows
Repeating, non-overlapping, fixed interval windows
Hopping Windows
Generic window, overlapping,

fixed size
Sliding Windows
Slides by an epsilon and produces output at the occurrence of an event

Manage out-of-order events

Manage actions on late arriving events


Working with Data Streams

Слайд 46Stream Processing Query Language
DML Statements
SELECT
FROM
WHERE
GROUP BY
HAVING
CASE
JOINS
UNION

Scaling Functions
WITH
PARTITION BY
Date and Time Functions
DATENAME
DATEPART
DAY
MONTH
YEAR
DATETIMEFROMPARTS
DATEDIFF
DATADD
Windowing

Extensions
Tumbling Window
Hopping Window
Sliding Window

Aggregate Functions
SUM
COUNT
AVG
MIN
MAX

String Functions
LEN CONCAT CHARINDEX
SUBSTRING
PATINDEX

Слайд 47Querying Events and Time
Every event that flows through the system has

a timestamp

User can pick it from the payload
SELECT * FROM TwitterStream TIMESTAMP BY CreatedAt

Or the system can assign timestamps automatically based on the event arrival time
SELECT * FROM TwitterStream

Projecting timestamp into payload
SELECT System.Timestamp AS Time, Text FROM TwitterStream

Note: System.Timestamp is a reserved property that exposes the timestamp of an event.

Слайд 48Tumbling Windows
SELECT TimeZone, COUNT(*) AS Count
FROM TwitterStream TIMESTAMP BY CreatedAt


GROUP BY TimeZone, TumblingWindow(second,10)

Tell me the count of tweets per time zone every 10 seconds

Repeating, non-overlapping, fixed interval windows

Слайд 49Hopping Windows
SELECT Topic, COUNT(*) AS TotalTweets, AVG(SentimentScore)
FROM TwitterStream TIMESTAMP BY

CreatedAt
GROUP BY Topic, HoppingWindow(second, 10 , 5)

Every 5 seconds give me the count of tweets and the average sentiment score over the last 10 seconds


1

5

4

2

6

8

7







0

5

20

10

15

Time
(secs)


25

A 10-second Hopping Window with a 5-second “Hop”


30


4

2

6

8

6



5

3

6

1

1

5

4

2

6

8

6

5

3

6

1

5

3

Generic window, overlapping, fixed size

Слайд 50Sliding Windows
SELECT Topic, COUNT(*) FROM TwitterStream
TIMESTAMP BY CreatedAt
GROUP BY Topic,

SlidingWindow(second, 10)
HAVING COUNT(*) > 10

Give me the count of tweets for all topics which are tweeted more than 10 times in the last 10 seconds



1

5







0

5

20

10

15

Time
(secs)

25

A 10-second Sliding Window


8

8

5

1

9


1

Slides by an epsilon and produces output at the occurrence of an event

Слайд 51Joining multiple streams
SELECT TS1.UserName, TS1.Topic
FROM TwitterStream TS1 TIMESTAMP BY CreatedAt
JOIN

TwitterStream TS2 TIMESTAMP BY CreatedAt
ON TS1.UserName = TS2.UserName AND TS1.Topic = TS2.Topic AND DATEDIFF(second, TS1, TS2) BETWEEN 1 AND 60
WHERE TS1.SentimentScore != TS2.SentimentScore

time

List all users and the topics on which they switched their sentiment within a minute

Слайд 52Pushing Data with REST APIs

Слайд 53Today’s easy visual analytics tools empower the business to make smarter

decisions and generate more business value
Fast, direct, agile access to big data to analyze in-place, blend with EDW, OLAP and personal data sources, decreasing long backlogs for faster actionable insight
Less need to move large volumes of data between platforms to ask new questions or perform analytics

Unlocking the Value of Data

Слайд 55Key Takeaways
Real-world use cases
Common design patterns
Best practices
Popular real-time analytics tools

Слайд 56© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other

product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

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