In this project, we use GridDB to create a Machine Learning platform where we Kafka is used to import stock market data from Alphavantage, a market data provider. Tensorflow and Keras train a model that is then stored in GridDB, and then finally uses LSTM prediction to find anomalies in daily intraday trading history. The last piece is that the data is visualized in Grafana and then we configure GridDB to send notifications via its REST Trigger function to Twilio’s Sendgrid.
In this tutorial, we will build a trivial linear regression model with the data stored in GridDB. We will begin with GridDB’s python-connector to insert and access the data. Afterwards, we will see how to retrieve and convert the data using pandas and numpy. In the end, we will train and visualize our regression model using scikit-learn and matplotlib.
The following tutorial is carried out on Ubuntu Operating system (v. 18.04) with gcc version 7.5.0. GridDB (v. 4.5.2) has been installed using their documentation available on Github.
Take a look at this half-tongue-in-cheek, half-heartbreaking website: http://www.iscaliforniaonfire.com/. Though I suspect it started out as a joke, the web page helps to illustrate — by sheer power of existence — the constant, existential threat faced by the Golden State; the state-wide wildfires can wreak havoc in countless ways, but today we want to focus on air quality. Specifically we want to look at whether carbon monoxide (CO) and/or Nitrogen dioxide (NO2) emissions rise when California is on fire, and by how much.
The full source code for this project can be found at the bottom of this page: FULL…
Imagine that you are the owner of a building complex. You have installed smart meters all over the place to monitor power usage. Each of the devices creates a timestamp two times in a minute. It saves power usage data in kW and timestamps in the form of epoch seconds. The IoT provider sends you a .CSV file every month.
You want to propose an energy-saving plan. To start, you’d like to identify patterns in power usage: how the power is consumed, when less of it is consumed, and when power usage is particularly extensive. …
Stock markets are fickle and often changing. Humans have tried to tame the bull throughout history but have never been successful. Stock market prediction is difficult because there are too many factors at play, and creating models to consider such variances is almost impossible. However, recent advances in machine learning and computing have allowed machines to process large amounts of data. This will enable us to use past stock exchange data and analyze trends. This post will leverage python and GridDB to analyze stock data for Google for the past year.
Stock prices are stored daily. Thus, daily stock data…
In this article we will discuss how to analyze and ingest a time series dataset with GridDB and Java. The data we will be analyzing is an open dataset that contains real estate property sales details. You can download the dataset from this link
First of all, let’s take a look at the structure of the dataset. You can have a proper idea on the dataset by referring to the following table.
Data in general is a large heap of numbers, to a non-expert these numbers may be more confusing than they are informative. With the advent of big data, even experts have a difficult time making sense of data. This is where visualisation comes in. Data Visualisation can be thought of as the graphical representation of information. Visual elements like charts, graphs and maps are often key to understanding trends in data and making data driven decisions. A good visualisation is often the best way to communicate results , after all, “a picture is worth a thousand words”. …
In this demonstration, we show how you to build a low cost Industrial Internet of Things (IIoT) solution using GridDB on a Raspberry Pi 4 with a Node-Red flow that uses MQTT to read temperature sensor data from an Industrial Shields M-DUINO 21+ Arduino PLC and then visualizes that data using Grafana.
For the hardware setup, we’re going to mount the hardware to a DIN rail, wire the temperature sensor, setup the Arduino IDE, and deploy our Arduino sketch to the PLC.
We mounted the Raspberry Pi4 to a DINRplate for a clean installation on a DIN rail and are…
In this blog, we will showcase GridDB’s Java Database Connectivity (JDBC) connection abilities by pairing it with the popular JDBC Python module. The module in this instance, JayDeBeApi, allows for the developer to connect their Python codebase to JDBC; this means that the developer will no longer be tied down to using only Java when interfacing with their favorite database API. To get the obvious stuff out of the way, please make sure you have GridDB installed, along with the Python client.
$ pip3 install griddb-python --user
In this demonstration, we show how you to build a low cost Industrial Internet of Things (IIoT) solution using GridDB on a Raspberry Pi 4 with a Node-Red flow that uses Modbus to read temperature sensor data from an Industrial Shields M-DUINO 21+ Arduino PLC.
First, we’re going to start by getting our Raspberry PI up and running. We need to install Ubuntu and build and install the GridDB Server, NodeJS, GridDB NodeJS Client along with Node Red and the Nodes we’ll be using.
GridDB needs to run on a 64-bit OS, so we’ll use Pi Imager to write a…