Business Platform Team

Thus far we’ve discussed how Cassandra, Spark, Kafka, Docker, and Kubernetes can be useful to build a global data platform. These components are powerful in their own right and managing them is a little simpler if we decide to use commercial components from DataStax and Confluent.

There are other tools and services we can use to further accelerate our timeline to deliver a world-class global data and analytics platform. Although bringing up a distributed data (Cassandra), distributed computing (Spark), and distributed communication (Kafka) is a great start for a framework, it still needs a few more components to make it a “Platform” which allows quick creation and delivery of services that an enterprise can use.

In my previous two blog posts, we went through the Sitecore XP0 9.1 installation process, reviewed some top common issues that come up while doing so, and proper ways to resolve them. If you have followed those steps correctly I am sure by now you have a successful instance for Sitecore running on your system.

With 83% of enterprise workload projected to be running on the cloud by 2020 as per Forbes, it is necessary for companies to have a definitive cloud adoption strategy. Also noteworthy is the distribution of projected adoption of the type of cloud with 41% of enterprise workloads to be running on the public cloud (AWS, GCP, IBM, Azure, and the likes). Another 20% are projected to be taking the private cloud route and 22% opting for the hybrid cloud.

Adoption of the cloud in a business setting is not an overnight exercise. Companies need to acquire the skills and knowledge before initiating the cloud migration process, which needs to be backed by a solid strategy. With proper planning and execution of the strategy, companies can start reaping the fruits of the cloud. The top practices, a company should adopt for successful cloud adoption are outlined below:

It’s much easier to iterate your platform on containers before deciding to use more “traditional” computing systems in Staging or Production. It’s not a requirement to use Docker or Kubernetes, but even if the system is using containers all the way up to Production, many of the DevOps cycles can be done more quickly because of how quickly environments can be refreshed.

Scraping website data is like a magic trick that lets you extract web data without having to copy and paste. It can all be done through some lines of code if you know basic Python syntax.

Large companies are using this technology to grow their business. Even Google scraps website data to analyze content and and rank them based on the relevance to your Google search. There are many use cases of web scraping in research, e-commerce, price comparison, market analysis, and lead collection. Regardless of the problem you’re trying to solve, these 5 open source libraries will help you scrape website data.