Cloud Data Platform Course

A cloud data platform grows with you

Cloud data platform architectures are designed for a fluxional and expansive data ecosystem. Instead of looking at rigid and static models such as the data warehouse or the database, a cloud data platform thrives on its capacity to re-calibrate according to customer needs. You can leverage a cloud data platform’s data processing capabilities to the fullest regardless of scale.

A cloud data platform integrates

Cloud data platforms can be fully interoperable, allowing customers to plug in their favorite (open-source) tools and work with them from within the platform. Cloud data platforms such as the data science studio by Record Evolution can also come with a fully operational built-in infrastructure and a complete toolchain covering all data-related processes starting with data harvesting and transforming the data in data pipes, all the way to reporting and visualizing.

The alternative to this would be purchasing and juggling between a variety of individual specialized tools. Apart from offering low user comfort, this solution may bring along the risk of vendor lock-in. It can also make it impossible to migrate your insights if you decide to switch to another tool. The bottom line with cloud data platforms is that you have one unified, holistically built environment that keeps your data, tools, and insights in one place so you can access them at any time.

It unifies your data

Ideally, a cloud data platform provides a data hub bringing together data from different sources. These include streaming data from IoT data routers, collecting data from web sources, FTP connections, S3 Buckets, Twitter sources, or even files. The data is then cleaned in data pipes, consolidated, and transformed to deliver the insights you need to extract. Find out how to establish connections with various data sources here.

It unites your data tasks

On the advanced analytics platform, you can see how the tasks of the data engineer, the data scientist, and the data analyst are united and work in concert to serve the data journey. You start with a data import from a variety of sources and move on to data transformation in pipes. Once you have obtained cleaned and structured high-quality data, you can start thinking about creating a data model. On the platform, you write code in workbooks using SQL, Python or Markdown and generate reports or infographics. You can also go one step further towards customizing, as in the case of custom infographics.

It transcends industry borders

Cloud data platforms are open-ended and customizable. You have built-in and/or integratable tools to structure, analyze, and generate insight from any kind of data. Customers across industry borders can make use of the built-in analytics tools to generate reports and visualize data. It’s not about the industry. It’s about the data, the things that data can do, and the experience of delivering high-quality outcomes based on that data in a secure environment. The Record Evolution data science studio is not built for just one particular type of business customer or one particular industry but can serve a variety of use cases.

It simplifies and accelerates 

But a cloud platform is also about user comfort. It reflects the drive to offer a fully seamless, one-gesture data journey. A collaborative setup and enabling architectures allow you to do more things and do them much faster. Automation capabilities and streamlined machine learning make it possible to speed up your development cycle significantly. With a simplified and custom automated flow, you have more space for sophisticated tasks and room for experimentation.

For the cloud: The Record Evolution data science studio

Built for the cloud, the Record Evolution data science studio has been created to encapsulate these virtues. A democratic, decentralized approach to data, the centrality of collaboration and a mindset of togetherness, as well as a focus on people and their data (or better still, data and its people), have been at the core of our vision.

And what is it that you, as an individual, can do on the platform?

Automate data extraction and data preparation

On the platform, you automate an array of data extraction and data preparation tasks to turn these into reproducible processes. Powered by embedded analytics, you can perform ad hoc querying, and optimize your data to serve as the basis for real-time decision making. A host of automatable loading, historization, versioning, monitoring, and control processes ascertains that basic data tasks are handled with guaranteed data quality and integrity.

Data historization and versioning mechanisms

The mELT processes ensure that data imports are always correctly merged with the existing stock. This is possible because the target table has additional knowledge of the objects it contains. If a file is loaded twice, mELT recognizes which items already exist and doesn’t make any changes the second time. If the second file contains corrections, mELT will update only the corrected data, maintaining strict version control.

Create a long-term data strategy 

The data science studio enables the creation and maintenance of a long-term data strategy. On the platform, users consolidate data from heterogeneous sources. The data is then used in a wealth of applications across company functions. As a unified service, the platform keeps all heterogeneous data constantly updated within a unified repository where the various data types are kept in their native formats. From there, data can be accessed for a wealth of tasks (such as the creation of machine learning models) and for running a variety of applications.

Speed up decision-making processes by collaborating on a single platform

Various teams within an organization can securely access the same data and collaborate on the platform directly. There is no need to copy data warehouses locally or move the data elsewhere. Rather than using locally copied data that is static and quickly becomes outdated, multiple users can leverage the platform data in real-time, making sure that they are accessing the latest update. Knowledge sharing takes place across physical boundaries. This creates a global environment for analytics that changes the way we think of business contexts and intra-organizational collaboration.