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Power Your Predictive Analytics with InfluxDB

If you're using InfluxDB to store and manage your time series data, you're already off to a great start. But why stop there? In our upcoming webinar, we'll show you how to take your data analysis to the next level by building predictive analytics using a variety of tools and techniques. We will demonstrate how to use Quix to create custom dashboards and visualizations that allow you to monitor your data in real-time. We'll also introduce you to Hugging Face, a powerful tool for building models that can predict future trends and identify anomalies. With these tools at your disposal, you'll be able to extract valuable insights from your data and make more informed decisions about the future. Don't miss out on this opportunity to improve your data analysis skills and take your business to the next level! What you will learn: Use InfluxDB to store and manage time series data Utilize Quix and Hugging Face to build models, visualize trends, and identify anomalies Extract valuable insights from your data Improve your data analysis skills to make informed decision

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| © Copyright 2023, InfluxData
Predictive Analytics
Using a Time Series
Database
Anais Dotis-Georgiou
July 2023
| © Copyright 2023, InfluxData
2
Anais Dotis-Georgiou
Developer Advocate
LinkedIn
| © Copyright 2023, InfluxData
| © Copyright 2023, InfluxData
Agenda
● What is Predictive Analytics?
● Time Series Databases
● Use Cases
● Using Quix, Hugging Face and InfluxDB 3.0 for forecasting
and anomaly detection
● Resources
What is Predictive Analytics
Time Series Databases
The age of instrumentation
Instrumentation
of the virtual world
(DevOps)
Sensors
in the physical world
(IoT)

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Power Your Predictive Analytics with InfluxDB

  • 1. | © Copyright 2023, InfluxData Predictive Analytics Using a Time Series Database Anais Dotis-Georgiou July 2023
  • 2. | © Copyright 2023, InfluxData 2 Anais Dotis-Georgiou Developer Advocate LinkedIn
  • 3. | © Copyright 2023, InfluxData | © Copyright 2023, InfluxData Agenda ● What is Predictive Analytics? ● Time Series Databases ● Use Cases ● Using Quix, Hugging Face and InfluxDB 3.0 for forecasting and anomaly detection ● Resources
  • 4. What is Predictive Analytics
  • 6. The age of instrumentation Instrumentation of the virtual world (DevOps) Sensors in the physical world (IoT)
  • 7. Characteristics of the data • Time-stamped • Generated in regular (metric) and irregular (event) time periods • Huge volumes • Real time and time sensitive
  • 8. Time series in every application Infrastructure & data sources Consumer & Industrial IoT Software Infrastructure Renewable & alternative energy systems Manufact uring & industrial platforms Fleet management & telematics Real-time Applications Developer Tools & APIs Kubernetes (K8s) DevOps Monitoring Gaming Applications Fintech Applications Network Monitoring TIME SERIES DATA
  • 9. Rise of time series as a category TIME SERIES RELATIONAL DOCUMENT SEARCH • Distributed search • Logs • Geo • High throughput • Large document • Orders • Customers • Records • Events, metrics, time stamped • for IoT, analytics, cloud native Time series is fastest growing data category by far Time series All others source: DB Engines
  • 10. | © Copyright 2023, InfluxData Characteristics of the data • Time-stamped • Generated in regular (metric) and irregular (event) time periods • Huge volumes • Real time and time sensitive
  • 11. | © Copyright 2023, InfluxData InfluxDB is 3 things API & Toolset POWERFUL for real-time apps HIGH PERFORMANCE MASSIVE for real-time data workloads of cloud & open source developers 1 2 3 Time Series Engine Community & Ecosystem
  • 12. | © Copyright 2023, InfluxData InfluxData Reference Architecture
  • 13. | © Copyright 2023, InfluxData | © Copyright 2023, InfluxData InfluxDB’s new storage engine is built on ● Rust ● Apache Arrow ● Apache Parquet ● Arrow Flight ● DataFusion
  • 14. | © Copyright 2023, InfluxData | © Copyright 2023, InfluxData Release Details 14
  • 15. | © Copyright 2023, InfluxData SQL Support 15
  • 18. Using Quix, Hugging Face and InfluxDB 3.0 for forecasting and anomaly detection
  • 21. | © Copyright 2023, InfluxData The Dataset
  • 22. | © Copyright 2023, InfluxData The Quix Pipeline
  • 23. | © Copyright 2023, InfluxData 23
  • 24. | © Copyright 2023, InfluxData Source Project 24
  • 25. | © Copyright 2023, InfluxData import influxdb_client_3 as InfluxDBClient3 client = InfluxDBClient3.InfluxDBClient3(token=os.environ["INFLUX_TOKEN"], host=os.environ["INFLUX_HOST"], org=os.environ["INFLUX_ORG"], database=os.environ["INFLUX_DATABASE"]) 25
  • 26. | © Copyright 2023, InfluxData def get_data(): # Query InfluxDB Cloud 3.0 while run: try: query = "SHOW TAG VALUES WITH KEY = "machineID"" table = client.query(query=query, language="influxql") machines = table["value"].to_pylist() for machine in machines: # this will end up loop when all data has been sent table = client.query(query=f"SELECT vibration, machineID FROM machine_data WHERE time >= now() - 5m AND machineID = '{machine}'", language="influxql") df = table.to_pandas() print(df) if df.empty: break # If there are rows to write to the stream at this time stream_producer.timeseries.buffer.publish(df) sleep(int(os.environ["task_interval"])) except: print("query failed") sleep(int(os.environ["task_interval"])) 26
  • 27. | © Copyright 2023, InfluxData Event Detection Project 27
  • 28. | © Copyright 2023, InfluxData Training the Model 28
  • 29. | © Copyright 2023, InfluxData 29
  • 30. | © Copyright 2023, InfluxData from huggingface_hub import from_pretrained_keras # Quix injects credentials automatically to the client. # Alternatively, you can always pass an SDK token manually as an argument. client = qx.QuixStreamingClient() print("Opening input and output topics") consumer_topic = client.get_topic_consumer(os.environ["input"], "default-consumer-group") producer_topic = client.get_topic_producer(os.environ["output"]) model = from_pretrained_keras(os.environ["model"]) 30
  • 31. | © Copyright 2023, InfluxData Forecasting Transformation Project 31
  • 32. | © Copyright 2023, InfluxData df = df.set_index('timestamp') data = df.drop(columns=['iox::measurement', 'machineID']) fit = Holt(data,damped_trend=True,initialization_method="estimated").fit(optimized=True) fcast = fit.forecast(10).rename("Multiplicative damped trend") fcast = fcast.reset_index().rename(columns={'index': 'timestamp'}) 32
  • 33. | © Copyright 2023, InfluxData Write Project 33
  • 34. | © Copyright 2023, InfluxData import influxdb_client_3 as InfluxDBClient3 client = qx.QuixStreamingClient() # get the topic consumer for a specific consumer group topic_consumer = client.get_topic_consumer(topic_id_or_name = os.environ["input"], consumer_group = "empty-destination") client = InfluxDBClient3.InfluxDBClient3(token=os.environ["INFLUX_TOKEN"], host=os.environ["INFLUX_HOST"], org=os.environ["INFLUX_ORG"], database=os.environ["INFLUX_DATABASE"]) def on_dataframe_received_handler(stream_consumer: qx.StreamConsumer, df: pd.DataFrame): # do something with the data here df = df.rename(columns={'timestamp': 'time'}) df = df.set_index('time') client.write(df, data_frame_measurement_name='mlresult', data_frame_tag_columns=['machineID']) print("Write successful") 34
  • 36. | © Copyright 2023, InfluxData Get Started 36 influxdata.com/influxdb-engine-beta/
  • 37. | © Copyright 2023, InfluxData InfluxDB Community Slack workspace Please join us in the InfluxDB Community Slack at www.influxdata.com/slack. To participate in conversations, join the #influxdb_iox channel.
  • 38. | © Copyright 2023, InfluxData 38 influxdata.com https://github.com/InfluxCommunity
  • 39. | © Copyright 2023, InfluxData 39 Try it yourself https://www.influxdata.com https://github.com/InfluxCommunity Get Started
  • 40. | © Copyright 2023, InfluxData Get Help + Resources! 40 Forums: community.influxdata.com Slack: influxcommunity.slack.com GH: github.com/InfluxCommunity Docs: docs.influxdata.com Blogs: influxdata.com/blog InfluxDB University: influxdata.com/university
  • 41. | © Copyright 2023, InfluxData T H A N K Y O U