Slides of comparisons between the Cloud-based Stream Analytics and InfuxDB Time-series analytics

1. Cloud-based Stream Analytics
VS
InfuxDB Time-series analytics

2. Our team
Gianmarco Cariggi Marco Costa Antonino Di Maggio
Andrea Littera Mattia Nicolella Leonardo Salvucci

3. Time series analysis comprises
methods for analyzing time series data in
order to extract meaningful statistics and
other characteristics of the data.
Time-series Analytics
Time series analysis accounts for the fact
that data points taken over time may have
an internal structure (such as
autocorrelation, trend or seasonal variation)
that should be accounted for.

4. Time Series Analysis
✔ Statistics
SCENARIOS
✔ Signal processing
✔ Econometrics
✔ Mathematical finance
✔ Predictions
✔ Control engineering
✔ Communications engineering
✔ Pattern recognition
✔ Weather forecasting

5. Streaming Analytics
Streaming analytics, also called event stream
processing, is the analysis of large, in-motion
data called event streams.
A Streaming Analytics platform can process
millions and tens of millions of events per
second.

6. Streaming Analytics
✔ Financial transaction
✔ Equipment failure
✔ Satellite
✔ Electric power grid
✔ Retail industry
SCENARIOS

7. ● Data: data set
● Order and time are
fundamental elements
● Usually dataset is a
set of past
observations
Time Series vs Streaming
● Data: flow of information
● Time may not be an
important feature of the
data
● It does not necessarily
encode data

8. Two technologies
Time-Series Analytics based
on Influx-DB
Cloud Based Stream Analytics
based on Microsoft Azure
DBMS for storing time series,
events and metrics
Globally distributed, horizontally
scalable, multi-model database
service

9. ● Both use a simple and declarative language that allows you to create
complex temporal queries and analyzes using simple SQL constructs
● Both offer a wide range of functions for data analysis and processing.
This query language supports simple data manipulation, aggregation
functions and much more.
Similarities
QUERY

10. Similarities
COMMON QUERY FUNCTIONS
AGGREGATE
● COUNT
● MIN
● MAX
● MEAN (Influx) -
AVG(Azure)
● SUM
DML STATEMENTS
● SELECT
● FROM
● WHERE
● GROUP BY
● HAVING
TIME FUNCTIONS
● DAY
● MONTH
● YEAR

11. Similarities
Azure Stream Analytics queries
SELECT TollId AS VehicleEntryTime FROM TollTagEntry WHERE ( CAST(TollId AS bigint)
BETWEEN 1 AND 2 )
Chronograf Analytics queries
SELECT mean(“co2”) AS “mean_co2” FROM “telegraf”.”autogen”.”surveys” WHERE time >
:dashboardTime:
QUERY EXAMPLES

12. Differences
QUERY RESULT

13. ● Both allow the possibility of creating customized dashboards, displaying
and monitoring your data of interest
● Both supports many different visualization type
Similarities
DASHBOARD

14. ● Both allow you to set up alerts to trigger an alert when certain conditions
are verified
Similarities
NOTIFICATIONS

15. Differences
DATA INPUT

16. Differences
STORAGE LIMITATIONS
Pay only for the storage you use
and the throughput you provision
globally. Throughput can be
scaled up or down depending on
the needs of your application as
low as a few 100 requests per
second to millions of requests
per second across any number
of Azure regions.
Open source core with closed
source clustering available either
on-premise or on the cloud as a
managed service. All offerings
are available via subscription.

17. Differences
SUPPORTED LANGUAGES
● .Net
● Clojure
● Erlang
● Go
● Haskell
● Java
● JavaScript
● JavaScript (Node.js)
● Lisp
● Perl
● PHP
● Python
● R
● Ruby
● Rust
● Scala
● .Net
● C#
● Java
● JavaScript
● JavaScript (Node.js)
● MongoDB client drivers written for
various programming languages
● Python

18. Differences
ACCESS METHOD
● HTTP API
● JSON over UDP
● DocumentDB API
● Graph API (Gremlin)
● MongoDB API
● RESTful HTTP API
● Table API

19. Other differences
Features STREAM ANALYTICS TIME-SERIES ANALYTICS
License COMMERCIAL OPEN SOURCE
Cloud-base only YES NO
Typing JSON NUMERIC DATA AND STRINGS
Secondary index YES NO
Server-side scripts JAVASCRIPT NO
Triggers JAVASCRIPT NO

20. Other similarities
Features STREAM ANALYTICS TIME-SERIES ANALYTICS
Data schema SCHEMA FREE SCHEMA FREE
Concurrency YES YES
Durability YES YES

21. Advantages
● Possibility of using it
locally
● Supports many
languages
● Unlimited free storage
● Data structure flexibility
● Events management
● Many access methods

22. Disadvantages
● Few access methods● Commercial license
● Supports less languages
● Limited paying storage

23. Thank You!
https://medium.com/@Nick1296/cloud-
based-stream-analytics-vs-time-series-
analytics-aad18c941bfb
Blog link: