The speed at which you can extract insights from your data is increasingly a competitive edge for your business. Data and analytics have to be at lightning fast speeds to seriously impact your user acquisition. Join this webinar featuring Forrester analyst Noel Yuhanna and Leena Joshi, VP Product Marketing at Redis Labs to learn how you can glean insights faster with new open source data processing frameworks like Spark and Redis. In this webinar you will learn: * Why analytics has to run at the real time speed of business * How this can be achieved with next generation Big Data tools * How data structures can optimize your hybrid transaction-analytics processing scenarios

1. Home of Redis
Analytics at the Speed of
Business with Redis and Spark
Leena Joshi
VP Product Marketing
Noel Yuhanna
Principal Analyst, Forrester

2. 2
Agenda
• Why Data & Analytics Need to be Real
Time
• Drivers and Challenges for Real time
analytics
• The Roadmap to Fast Data
• Recommendations
• Brief Introduction to Redis
• Analytics with Redis
• Redis –Spark Integration
• Making Analytics Cost Effective
• Extended analytics with Redis Modules
Noel Yuhanna – 20 min Leena Joshi – 20 min

3. Running Analytics At The Speed Of
Your Business
Noel Yuhanna, Principal Analyst
RedisLabs Webinar

4. © 2016 Forrester Research, Inc. Reproduction Prohibited 4
Data bottlenecks are creating
business bottlenecks that’s
impacting growth and innovation!

5. © 2016 Forrester Research, Inc. Reproduction Prohibited 5
Currency Oil
Digital transformation is all about the data…
But what if your data is slow and that’s not being
utilized for analytics or in a timely manner?
Data is the new

6. Today business users think of analytics as a set of boring reports
and dashboards … they don’t want yesterdays data tomorrow!
of enterprise data
in used for analytics….
12%

7. Source: Forrester
Performance remains a key Database challenge..

8. © 2016 Forrester Research, Inc. Reproduction Prohibited 8
Trends affecting your Database strategy..
Database
› Increasing transaction volume
› Data volume explosion
› Continuous 24x7 availability
› Stronger security measures
› All types of data formats
› New analytical requirements
› Faster access to information
› Co-related/unified data access
› More self-service capabilities
› Unpredictable workloads/patterns
DatabaseDatabase

9. © 2016 Forrester Research, Inc. Reproduction Prohibited 9
Businesses want real-time access to information…
› Mobile devices – we need data now!
› Competitive pressure – to act more quickly
› Pressure from businesses (LOB) - to support real-time data access
› New insights, advanced analytics – real-time BI
› Global business – that needs global real-time access
› IOT Applications – sensors, devices . .
› Lower cost of memory and computing

10. © 2016 Forrester Research, Inc. Reproduction Prohibited 10
TREND – The need for Fast Data
Real-time
Mostly
Batch
FAST DATA

11. © 2016 Forrester Research, Inc. Reproduction Prohibited 11
What is Fast Data?
Fast Data is combining Systems of Engagement (batch) and
Systems of Record(Real-time) together quickly to support
new next-generation business analytics.
Systems of engagement (SOE)
• Mobile, web, and smart devices
• Frequent changes
• Delight clients
• Delivered frequently
Systems of record (SOR)
• Stable requirements
• Highly transactional
• Less change
• Delivered infrequently
Forrester estimates that 20% of all data in an enterprises
is Fast Data, and that’ll double over the next three years.
Fast
Data
Traditional DataReal-time data

12. © 2016 Forrester Research, Inc. Reproduction Prohibited 12
Key capabilities you need for Fast Data Strategy
› Distributed In-memory computing layer
› Low-latency access to large volumes of data
› Ability to integrate data from disparate data sources
› Continuous availability of the database/data platform
› Support for scale-out architecture to support extreme scale
› Ability to support hybrid environment – on-prem and cloud
› Easy to deploy, highly automated and with built-in intelligence

13. © 2016 Forrester Research, Inc. Reproduction Prohibited 13
Apache Spark offers new possibilities…
› Open Source distributed computing framework that uses in-memory
platform to scale, process and provide low-latency access
› Key benefits: i) Performance, ii) Supports streaming and complex
analytics, iii) Supports SQL, iv) Easy to write Apps using Java, Scala or
Python.
› Use cases: i) Sensor data processing, ii) Stream processing, iii)
Interactive analytics and data processing platform, iv) Interactive
algorithms in machine learning, v) IOT analytics, vi) Complex analytics.
› Adoption: Current adoption of Apache Spark is estimated at 30% in
large enterprises likely to double in the next three years.

14. © 2016 Forrester Research, Inc. Reproduction Prohibited 14
Road Map for your Fast Data Strategy

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Recommendations
› In the era of big data, you need to look beyond traditional data
architectures to succeed and gain competitive advantage.
› Fast data strategy needs to be on your roadmap, focusing on making
data available more quickly to business users and decision makers.
› Look for automation, simplification and easy-of-use database solutions
that can help support faster time-to-value initiatives.
› Look at in-memory and scale-out architectures to support new
business analytics to grow business and innovate.
› Look at open source that can provide lower cost and deliver a platform
to support your fast data strategy.

16. © 2009 Forrester Research, Inc. Reproduction Prohibited
Thank you
Noel Yuhanna
www.forrester.com
Twitter: @nyuhanna

17. 17
Who We Are
The open source home and commercial provider
of Redis
Open source. The leading in-memory data
structure store, supporting any high
performance operational or analytic use case.

18. 18
Redis is a Game Changer
Simplicity
(through Data Structures)
Extensibility
(through Redis Modules)
Performance
ListsSorted Sets
Hashes Hyperlog-logs
Geospatial
Indexes
Bitmaps
SetsStrings
Bit field

19. 19
• Used by developers like “Lego” blocks
• Enables data to be processed on the database level
rather than the application level
• Turns complex functionality into a single command
such as:
"Get the e-mail address of the user with the highest score in a game that
started on July 24th at 11:00pm PST”
ZREVRANGE 07242015_2300 0 0
Simplicity: Data Structures - Redis’ Building Blocks
ListsSorted Sets
Hashes
Hyperlog-
logs
Geospatial
IndexesBitmaps
SetsStrings
• Enable solving complex problems by creating relations between
data structures, using standard or custom (Lua) commands
• The result: cleaner, more elegant code, faster execution time

20. 20
Extensibility: Modules Extend Redis Infinitely
• Add-ons using a Redis API for seamlessly adding to
it use cases and data structures
• Modules enjoy Redis’ simplicity, super high
performance, infinite scalability and high availability
• Modules can be created by anyone. Certified by Redis Labs.
Full Text Search Enhanced JSON Graph Operations Secondary Indexes
Linear Algebra SQL Support Image Processing
N-Dimension
Queries …

21. 21
Performance: the Most Powerful Database
Highest Throughput at Lowest Latency
in High Volume of Writes Scenario
Lowest number of servers needed to
deliver 1 Million writes/second
300
50 50
2
0
50
100
150
200
250
300
350
Benchmarks performed by Avalon Consulting Group Benchmarks published in the Google blog

22. 22
Redis Cloud
Available since mid-2013
6,100+ enterprise customers
Redis Labs Enterprise Cluster (RLEC)
Available since early-2015
100+ enterprise customers
Wide Adoption

23. Why Use Redis in Analytics

24. 24
Popular Redis Use Cases
Geo SearchData Ingestion Social Functionality
Following, Followers, Relations Location-based ApplicationsHigh Throughput Buffering
Job & Queue Caching
Any Business Application Any Web or Mobile App
High Speed Transactions Time-Series
Business Applications
Analytics
Real-time Computations Time-Based Analysis

25. 25
Example : Redis For Bid Management
The Application Problem
• Many users bidding on items
• Need to instantly show who’s
leading, in what order and by how
much
• May also need to display analytics
like how many users are bidding in
what range
• Disk-based DBMS-es are too slow for
real-time, high scale calculations
Why Redis Rocks This
• Sorted sets automatically keep list of
users and scores updated and in
order (ZADD)
• ZRANGE, ZREVRANGE will get your
top users
• ZRANK will get any users rank
instantaneously
• ZCOUNT will return a count of users
in a range,
• ZRANGEBYSCORE will return all the
users in a range by their bids

26. 26
Redis Sorted Sets
ZADD item:1 10000 id:2 21000 id: 1
ZADD item:1 34000 id:3 35000 id 4
ZINCRBY item1:1 10000 id:3
ZREVRANGE item:1 0 0
id:3
Item: 1
id:3 44000
id:4 35000
id:1
id:2
21000
10000

27. 27
Example : Redis For Recommendations
The Application Problem
• Users, items, likes, dislikes, similarities
• Set comparisons of user likes, user
dislikes should help create similarity
scores, which can then be stored in a
sorted set
• Set comparisons of similar user
likes/dislikes with items not purchased
by current user should yield suggestions
• High speed and low latency
requirements
Why Redis Rocks This
• Redis Sets are unordered collections
of strings- SADD to add objects to
each tag
• Set operations executed in –
memory, blazing fast speeds
• SINTER, SINTERSTORE to intersect
multiple sets
• SUNIONSTORE to add multiple sets
• SISMEMBER to determine membership,
SMEMBERS to retrieve all values
• Sets and Sorted sets combined are a
great choice for recommendation
engines

28. 28
Redis Sets
SADD item:1 tag:1 tag:22 tag:24
SADD tag:1 item:1
SADD tag: 2 item:22 item:14 item:3
SINTER tag1 tag2
item:3
SUNIONSTORE tag:x tag1 tag2
SMEMBERS tag:x
item:1 item:3 item:22 item:14 item:3
item 1 {tag:1, tag:22, tag:24}
{item:1, item:3}tag 1
{item:22, item:14, item: 3}tag 2
{item:1, item:22, item:14, item: 3}tag x

29. Redis & Spark

30. 30
Spark & Redis – Serving Layer & Accelerator
Internal accelerator

31. 31
Accelerate Spark Time-Series with Redis
Redis sorted sets accelerate time series data
processing by 100 times compared to other in-
memory K/V stores
Example time series data: Stock prices for 1024
stocks over 32 years

32. 32
Accelerating Spark Time-Series with Redis
Redis is faster by upto 100 times compared to HDFS
and over 45 times compared to Tachyon or Spark

33. 33
More Details About the Redis & Spark Integration
Github link: Spark-Redis Connector Package
https://github.com/RedisLabs/spark-redis
How to get started with Spark and Redis:
https://redislabs.com/solutions/spark-and-redis
Blog: https://redislabs.com/blog/connecting-spark-
and-redis

34. Cost Effective Analytics

35. 35
Price/Performance of Memory Technology

36. 36
Redis on Flash
Flash used as a RAM extender and NOT as persistent storage

37. 37
How to Achieve Optimal Price/Performance
By dynamically setting RAM/Flash ratio Behind the scenes…

38. 38
Single Server Results with Dell & Samsung NVMe
read
write
read
write
Avg: 2.04M ops/sec
Max: 2.14M ops/sec
Avg: 0.91msec
Max: 0.98 msec
% below 1msec: 100%
Avg: 313RMB / 9.4WMB
Max: 1.71RGB / 96WMB
Avg: 1.45Gbps (Tx) / 0.97Gbps (Rx)
Max: 1.6Gbps (Tx) / 1.2Gbps (Rx)
Test setup:
• Redis Labs Enterprise
Cluster v3.2
• Dell Xeon CPU E5-
2670 v3 @ 2.50GHz
• 4x Samsung NVMe
PM1725
• Memtier benchmark-
open source tool
• 100B object size
• 80% read
• 20% write
Throughput – ops/sec
Latency – msec
Disk Bandwidth – MB/sec
NW Bandwidth – Gb/sec
>2M Ops/sec, <1 ms latency, > 1GB disk bandwidth

39. 39
Customer Example : Redis on Flash
• Genome dataset: 31TBs of raw data
• Optimized data set through encoding
and using Redis Hashes
• Resulting data runs high speed
analyses with 55GB of RAM
and 4.5TB of Flash
• 97% annual savings compared to a
pure RAM solution
Redis on RAM Redis on Flash
RAM Size 5TB 0.5TB
Flash size N/A 4.5TB
Servers
on AWS :
21x r3.8xlarge
on P8:
2x s822 LC
1yr costs $489,333 $15,677
P8 savings 97%

40. Extending Redis Analytics
40

41. 41
What Can Modules Do
41
• All modules are certified by Redis Labs for full compliance with OSS
Redis, Redis Cloud and Redis Labs Enterprise Cluster (RLEC)
Full Text Search Enhanced JSON Graph Operations Secondary Indexes
Linear Algebra SQL Support Image Processing
N-Dimension
Queries …

42. 42
42
3.15
2.40
21.00
8.70
24.57
10.61
0.00
5.00
10.00
15.00
20.00
25.00
30.00
Full text search Prefix search
Average Latency (msec)
RLEC Elasticsearch Solr
20,045
6,831
690
3,686
621
3,133
0
5,000
10,000
15,000
20,000
25,000
Full text search Prefix search
Ops/sec
RLEC Elasticsearch Solr
85% higher
32x higher
7.8x faster 4.1x faster
redisearch
The world fastest text search engine

43. 43
Redis Module Hub (www.redismodules.com)

44. 44Redis Labs proprietary & confidential information
Next Steps
Learn More:
Redis with Spark: https://redislabs.com/solutions/spark-and-redis
Redis on Flash : https://redislabs.com/solutions/redis-for-very-large-
datasets
Redis Modules : www.redismodules.com
44

45. Home of Redis
Questions?
@socialeena