The document introduces InfluxDB 3.0 and InfluxDB Cloud Dedicated, highlighting their optimizations for handling time series data at scale, with features like sub-second query responses and built-in data lifecycle management. It emphasizes the platform's adaptability for various workloads, security, and cost-effectiveness, alongside its use of modern technologies such as Apache Arrow and Parquet for efficient data processing. The target industries for adoption include gaming, fintech, and industrial IoT, showcasing its versatility across different applications.

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Introducing:
InfluxDB Cloud
Dedicated
May 2023

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Introductions
Gary Fowler
Sr. Product
Manager @
InfluxData
Balaji Palani
Vice President,
Product Marketing
@ InfluxData

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Agenda
3
● Introducing InfluxDB 3.0
● InfluxDB Cloud Dedicated
● See it in Action

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Businesses are defined by
the experiences they deliver

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Time series data is a foundational
element of
applications & services

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Types of time series data
Quantitative values
collected regularly
over time.
State changes or
values generated
irregularly over time
Complete event or
request propagation in
a distributed system
Metrics Events Traces

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Time series data
When Where
from sources
Sources, networks, infrastructure & applications
Based on time
Hours, minutes, seconds & nanoseconds

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Building applications in two worlds
Physical Virtual

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Data…
arrives quickly
at massive scale
in real-time
with context
Applications…

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Most general-purpose
databases simply cannot handle
time series data at scale

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Why do
general-
purpose
databases fail
for time series?
• Optimized for processing
transactions (OLTP)
• Not built for time series workloads
with heavy writes and reads
• Data lifecycle management such as
retention is not built-in

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Purpose-built for time series data
Scale
Designed to scale for large volumes of time series data
Distributed
Non-blocking high-volume writes and reads
Availability
Write and read availability are prioritized over consistency
Management
Data lifecycle management with built-in data retention

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InfluxDB 3.0: Built for real-time analytics, speed,
scale, low cost
Designed to
deliver
sub-second
query responses
Deliver awesome
end user
experiences
One data store
for metrics,
events & traces
Faster time to
learning and
insights
Store data with
highest
compression on
low-cost object
store
Lower overall
costs of
ownership (TCO)
Faster Time to
Awesome® with
SQL, InfluxQL &
developer tools
Improve
developer
productivity
Open &
interoperable
with data
ecosystem
Improve data
efficiency

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InfluxDB 3.0: Eliminated cardinality as a constraint
• Catalog to track schema
changes and statistics
• Time-based partitions
• Optimizations to find “needle in
a haystack” quickly
• Benefits:
• Unbounded cardinality.
• Easily store wide tables or high
dimensional time series data (e.g.
tracing data)

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InfluxDB 3.0: Columnar “hot” & “cold”
storage tiers for performance & low cost
Cold
Optimized for
lowest cost long-
term storage
Optimized for low
latency analytical
queries
Data in memory Data in object store
seconds, minutes, hours weeks, months, years
Hot

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InfluxDB 3.0: Open architecture for interoperability
Machine
Learning Tasks
Data Science
Activities

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InfluxDB use cases
Metrics data lake
for monitoring
Ingest, analyze and
correlate in real-time,
operational time series
data from systems,
networks, infrastructure,
services and applications.
EXAMPLES:
Network Monitoring, Infrastructure
Monitoring, DevOps Monitoring
etc.
Real-time analytics
for IoT
Collect, transform, analyze
and predict in real-time,
time series data from
sensors connected to
internet.
EXAMPLES:
Predictive Analytics,
Sensor Monitoring,
Energy Monitoring etc.
Analytics SaaS
Applications
Build analytics SaaS
(software as a service)
applications such as in
devops / monitoring space
using time series data.
EXAMPLES:
Log Analytics Platform,
Tracing as a Service etc.

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Customer adoption by industry
Gaming & Entertainment
Sustainability / Clean Energy
Dev Tools & APIs
Industrial IoT
zzzzzzz
Fintech
Cloud Services
Consumer IoT
Crypto
Network Telemetry

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InfluxDB Platform

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InfluxDB 3.0: Run on cloud & on-premises

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InfluxDB Cloud
Dedicated

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What is InfluxDB Cloud Dedicated?
Fully managed cloud solution
Single-tenant solution optimized for time series workloads
Designed for flexibility and scale
Designed to handle large workloads
Enterprise-grade security
InfluxDB Cloud Dedicated protects your data at rest and in
motion
Straightforward, capacity-based pricing
InfluxDB Cloud Dedicated offers annual, pre-paid contracts
based on cluster tier (CPU and RAM) and storage needs

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Why InfluxDB Cloud Dedicated?
Performance
Optimize Your Workloads
Scalability
Growing Workloads
Location
Data where you need it
Edge
Reliable Distributed Systems
Different workloads
have different needs.
InfluxDB Cloud
Dedicated lets you
customize your cluster
configuration so you
can optimize your
database to prioritize
writes or queries.
If you have
medium-sized data
workloads and expect
them to grow, Cloud
Dedicated is right for
you. Whether your
workload relies more
on ingest, storage, or
both, Cloud Dedicated
can grow with you.
Will be available on all
supported regions* on
AWS, Azure**, and
GCP** public clouds,
Cloud Dedicated helps
customers meet
regulatory and data
residency requirements
by storing data in
specific region(s).
Create seamless, durable
queues that automatically
replicate data from single-
node InfluxDB instances at
the edge to your Cloud
Dedicated instance. Use
the same data to deliver
unique insights to different
stakeholders.

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Demo

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In this demo -
Real-Time
Analytics
Use Case
➔ See querying with both SQL and InfluxQL
➔ See how easy it is to use Apache Arrow &
Arrow Flight to get data from InfluxDB 3.0
➔ See how fast it is for a Data Scientist or
Business Analyst to get a large set of (0
TTBR) data into a Pandas Dataframe for
analysis
➔ See how easy it is to get that data in a
Pandas dataframe or saved into a Parquet
File

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Apache Arrow
Apache Arrow is a framework for defining in-memory columnar data
● Language-agnostic standard for
columnar memory
● Efficient for running large
analytical workloads on modern
CPU and GPU architectures

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Apache Parquet
Apache Parquet is column-oriented data file format designed for efficient
data storage and retrieval
Minimizes disk usage while storing
gigabytes of data
https://dzone.com/articles/how-to-be-a-hero-with-powerful-parquet-google-and
Efficient retrieval and deserialization of
large amounts of columnar data

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Flight SQL
InfluxDB Cloud Dedicated uses the Apache
Arrow Ecosystem

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Let’s see it in action

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InfluxDB 3.0: Fast, Real-Time & Cost Effective
Reduce Time
to Insights
Store More
Data
Reduce
Storage Costs
Better Ingest
Performance
Datapoints per
second
50% 10X 70%
10X 100M+
Sign up for
Free

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Q&A

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T H A N K Y O U