When it comes to getting value from data in 2018, it's safe to say that we've come a long way from the huge traditional warehouses and networks. The accelerating interest in things like machine learning, IoT, and the cloud means that data science is finally getting the attention it deserves, and as such, big brands are beginning to take notice.
Perhaps the biggest information-based giant of them all is Google, with its huge search engines and incredible devotion to in-depth information. To assist people with tapping into all the benefits of information, Google launched their own interactive developer tool in 2015 called "Datalab" - a system that helps to visualize and analyze data in a matter of clicks.
Some experts estimate that around 80% of a company's total workload process time is devoted to the analysis and preparation of data. This means that developers are spending too much time making sure that data is ready to be assessed, and not enough time benefitting from the information available. The Google Cloud Datalab system could be a solution to this, helping developers to get raw insights from data while exploring, publishing, and sharing valuable reports in a cost-effective and efficient way.
Using a service called Jupyter Notebooks, otherwise known as iPython, Google has designed a system that allows developers to create documents featuring live visualizations and codes. In the data science world, Jupyter is a very well-known system, and the active forum around it should mean that it's easier for developers to get started with and explore the tool.
Using Datalab from the Google Cloud Platform, developers will have the opportunity to truly get behind the scenes with their data, transforming and processing the information that exists in their cloud storage, compute engine, and BigQuery services. The information gathered should make it easier for companies to develop the data pipelines they need to enhance BigQuery, and even create their own models for machine learning.
At its core, Google's Datalab system is an interactive tool all about exploring and visualizing data. It's perfect for building machine learning strategies on Google Cloud, and it runs on the Google Compute Engine to ensure that it's ready to connect with various existing cloud services. This means that tapping into your data is easy.
Some of the features you can expect to see from the Google Cloud Datalab service include:
Full Integration: The Google Cloud Datalab service makes it easier to process and manage data gathered with BigQuery, Cloud Storage, Cloud Machine Learning, and even Stackdriver monitoring systems. With simplicity at your fingertips, you can focus more of your efforts on development and exploration.
Notebook format: To make data organization even simpler, the Cloud Datalab system comes with a feature to combine results and code, along with visualization in a notebook format.
Simple visualization: With Matplotlib or Google Charting you can create visualizations within a matter of minutes.
Pay-per-user: Because the Google Cloud Platform is all about cost-efficient innovation, you'll only have to pay for the resources that you use while you're in the cloud.
Machine learning: In a world where AI is becoming more compelling than ever, the Google Cloud Datalab supports the TensorFlow-enabled machine learning models to help you create your own machine learning experiences.
iPython Support: Datalab is based on the former iPython system, Jupyter, which means that you can use a range of pre-existing packages on the web when it comes to getting statistics. You can also enjoy interactions with an active community online.
Interestingly, the Google Cloud Datalab isn't a full platform in itself, but a docker image that was designed and maintained by the Google company. If you want to use Datalab yourself, you'll need to start by deploying the system as an application on the App Engine, which is where you'll begin to pay for the service too.
Once you're deployed, you'll be able to start your own projects and develop notebooks that are designed to gather and showcase information about your chosen data. There's some overlap between the Datalab system and other intelligence tools available on the market, so it shouldn't be too difficult to get used to the intelligence experience. Here are just some of the biggest benefits of working with Google Cloud Datalab:
It's Integrated and Open Source: As noted above, the Datalab is built on the Jupyter system, which comes with a thriving knowledge base and a vast ecosystem of modules to tap into. Plus, you can access data on all of your Google systems, like Google Compute Engine, BigQuery, Cloud Storage, and more.
Scalability: Regardless of how much information you need to assess, you can rest assured that you'll be able to read it with Google Cloud Datalab. The system is scalable to suit your needs as a growing company.
Machine learning: Create your own perfect machine learning models ready to predict trends based on your information. Explore and build the perfect solutions for your company.
Visualization and management of data: With cloud Datalab, you can get an incredible insight into your most important data, visualizing trends with Cloud Storage, BigQuery, and Python.
If you're ready to tap into the true value of big data for your brand, then Google Cloud Datalab could be the perfect way to get started. To find out more, why not call the experts here at Coolhead Tech? We can help you make your journey into the cloud.