In the last post, we talked briefly about the Oracle Digital Assistant. In this post, I would like to walk you through the provisioning process of the Autonomous Oracle Digital Assistant.
If you are following the Oracle Autonomous Mobile and Chatbot Platform announcements you would have now realised that we have announced the availability of the Oracle Digital Assistant platform as a new SKU under the PaaS offerings.
In this post, I will delve deeper into the Oracle Digital Assistant offering and answer what I anticipate will be common questions about the changes.
The other day my boss shared with me a nice trick that I found it very useful and has simplified my life a lot, when creating demos or quick prototypes. For this reason and knowing how many people are quickly switching into using Oracle Autonomous Database (ADW or ATP), I thought it would be a good idea to be shared as a quick reference.
The trick consists in using SQL Developer to easily import Data from Excel spreadsheets, directly into Oracle Autonomous DB (ADW or ATP). This also opens up a nice wizard that helps create and configure new database tables to be created and then used to import the data. How cool is that?
This complements a previous article that explains how to provision and get started with Oracle Autonomous Transaction Processing Database. Also, we have published articles to get started with Oracle Autonomous Data Warehouse.
Remember, the difference is simple:
The latest VBCS 2.0 release makes developing Mobile Applications faster using a declarative drag and drop approach while giving the flexibility to the developer to inspect the code and inject any additional code that the tool doesn’t provide out of the box. You can build your app from the browser itself without the need for any code editor.
Here is a quick tutorial on how to build a Mobile app using VBCS.
Once you develop your mobile app, you can create an Android or iOS build profile that will allow the tool generate the QR code so that you can test your mobile app on the device of your choice.
A few days ago, we published an article that shows how to provision and connect to Oracle Autonomous Transaction Processing Database (ATP). Based on this, we got multiple requests to also demonstrate how to extend the connection to the Autonomous DB, not only from SQL Developer, but also from polyglot microservices.
In this blog, we are going to take a step forward and create a simple “Hello World” NodeJS application that exposes some REST APIs that push and pull data using an Oracle Autonomous DB. The idea is to give you all knowledge required, to be start building your own microservices, consuming data from Autonomous DB.
With all the recent exciting releases of Oracle Autonomous PaaS Services, I wanted to explore some of the client connectivity options to work against the Oracle Autonomous Data Warehouse (ADW).
In my cloud subscription I provisioned an instance of ADW which took less than two minutes from start to finish – terrific, now I am ready to leverage the functionality. If you want to know the steps to provision an ADW instance check out this blog post – https://redthunder.blog/2018/07/02/teaching-how-to-get-started-with-oracle-autonomous-data-warehouse-cloud-service/
Obviously an empty data warehouse isn’t particularly useful so one of the first things I wanted to do was to connect a SQL client to the ADW instance so that I can load some data. Initially I used Oracle SQLDeveloper to load data into my ADW instance. I had staged my Excel data files into an Oracle Cloud Object Storage container and then referenced them in my SQL code as External Tables. Carlos has already blogged the steps required for this in https://redthunder.blog/2018/07/02/teaching-how-to-get-started-with-oracle-autonomous-data-warehouse-cloud-service/ . If you follow the steps you will quickly get your ADW instance populated with your data. In fact for the demo scenario my ADW instance was now populated with some data (approx. 1 Million rows of Sales data and associated related dimensions (Product, Customers etc).
My next step (and the subject of this blog post) was to use the Oracle Instant Client in order to query the loaded data. Of course I could easily have viewed the data inside SQLDeveloper but I wanted to try some other approaches. Often in Proof of Concepts there is a need to quickly spin up a tool to create, retrieve, update and delete data. Anyone who has used the Oracle Database would be familiar with the SQL*Plus client which is included as part of the Oracle Instant Client. For those who are not familiar with Oracle Instant client, the Oracle website describes it as follows,
“Free, light-weight, and easily installed Oracle Database tools, libraries and SDKs for building and connecting applications to an Oracle Database instance. Oracle Instant Client enables applications to connect to a local or remote Oracle Database for development and production deployment. The Instant Client libraries provide the necessary network connectivity, as well as basic and high end data features, to make full use of Oracle Database. It underlies the Oracle APIs of popular languages and environments including Node.js, Python and PHP, as well as providing access for OCI, OCCI, JDBC, ODBC and Pro*C applications. Tools included in Instant Client, such as SQL*Plus and Oracle Data Pump, provide quick and convenient data access.”
I used a Vagrant-Box / VirtualBox to avoid having to install development tools such as the Oracle Instant Client directly on my laptop operating system. I found an existing vagrant box that provided me with an Oracle Linux base that also included Docker. This vagrant-box image allowed me to quickly spin up a base environment which in turn allowed me to focus on steps to run the Oracle Instant Client inside a Docker container inside the Virtual Box environment (sounds like a cheesecake recipe – lots of layers). The Dockerfile I used was based on the Oracle Instant Client forked from the official Oracle DockerImages project with some modifications for specifics around connecting to an Oracle Data Warehouse Instance. Continue reading “Connect Dockerised Instant Client to Autonomous Data Warehouse”
We all know that data is massively valuable to businesses, whether it is to support daily business transactional activities (Online Transaction Processing – OLTP), or to help business with planning, problem solving and decision making (Online Analytical Processing – OLAP). Either way, businesses heavily rely on both ways to support their most important strategies and activities.
Until recently, companies had to heavily invest in provisioning, securing, patching and driving either way of Online Data processing mechanisms. In most cases, even with Cloud adoption, companies still had to rely on their own skills to make sure that their databases were properly patched, secured, tuned and managed.
However, today there is another option with the recent announcements that Oracle have made around Autonomous Databases for both OLAP and OLTP data processing. What this means, is that Oracle has taken automation to a totally new level with the assistance of Machine Learning. The idea is that the DB itself is self-sufficient with a full set of automated activities that range from patching, securing, optimising, etc. This will reduce not only the effort to run data workloads, but removing completely human errors, creating the opportunity to not only keep the lights on, but to focus on crucial business activities around innovation and differentiation.