Designing Cloud Data Platforms

Designing Cloud Data Platforms – When it comes to integrating analytics data into the cloud, the traditional approach to the heart of a data warehouse solution is like putting all your eggs in one basket.

The advent of the cloud has fundamentally changed the world of data analytics – enabling the integration, transformation and analysis of data from any source at any time. But designing an innovative data analytics platform can be a challenge

Designing Cloud Data Platforms

Designing Cloud Data Platforms

This article combines our experience with more than 100 innovative data platform design projects using Google Cloud as the cloud platform. In this article, Pythian SVP of Analytics Linda Partners gives you an introduction to the data platform. Read this article to know about:

Best Practices For Designing, Developing, And Deploying A Data Warehouse

Now that you understand the value of building a data platform at Google, you’ll likely want to read more about implementation best-practices. In this article, we describe the introduction of Google Cloud Services, why you should use a data platform, and not just a warehouse, and how to map the cloud layers to the Google Cloud Platform.

Designing, implementing, and managing a cloud data platform is complex, but Google-Cloud certified experts like us can help with every step of your data journey. Learn more about Pythian Cloud Solutions for Google Cloud

The advent of the cloud has fundamentally changed the world of analytics, making it possible to integrate, transform and analyze any type of data from any source at scale. But designing these enterprise data platforms, especially when cloud services are changing almost as quickly, can be a challenge.

We, at Pythian, have been at the forefront of this new world for six years and have been involved in nearly 100 projects – enough to say what we do. We spend a lot of time designing cloud data platforms across all public platforms, and we know that there is a lack of a fully integrated content on how to do it well.

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This article brings together many of our thoughts on cloud data platform design, and while the concepts are relevant to platforms that can run on any public cloud platform for a number of good reasons, we’ve removed them for the purposes of this article. Google Cloud released. things For example Google Cloud

When it comes to integrating analytics data into the cloud, the traditional approach of making data storage the heart of the solution is like cutting off your nose and putting all your eggs in one basket.

How cloud data warehousing differs from traditional data storage, and how cloud services facilitate a data platform is more than just data storage – it also includes data storage as part of the overall solution.

Designing Cloud Data Platforms

A cloud database is highly scalable and elastic, with pricing directly proportional to the amount of processing you do on the storage. It can create the false impression that in the cloud, because you have the largest and most expensive data warehouse at your disposal, you can put all your data into it, as you have always done, and go from there. But the traditional way of bringing all your data into a single data warehouse is flawed for a number of reasons, including:

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The bottom line is that thanks to some truly amazing cloud services, you can now build a modern and modular data analytics platform that takes advantage of the elasticity and benefits of the cloud, including a cloud data warehouse. The platform provides data storage, but also provides cost-effective storage of any volume and variety of data you encounter. This allows direct access to that raw, unstructured data for activation in other systems and exploration by advanced users and data scientists.

A cloud-native data platform is an analytics infrastructure solution that can cost-effectively store, integrate, transport and manage virtually unlimited amounts of any type to facilitate actionable analytics results.

In a data platform, the data lake is often combined with a data warehouse that becomes a place for analytics, but the data platform can manage and deliver value in addition to the data warehouse and, in some cases, without a data warehouse. General

To learn more about why a data warehouse is not as feature-rich and flexible as a data platform alone, check out our e-book “The Database Is Dead, The Data Platform Lives.”

Cloud Data Lake House

To achieve maximum performance and return on investment, your data analytics platform should be designed with the following features in mind:

A cloud data analytics platform is a system This means that it has some inputs and outputs, with the expectation that some inputs map to some outputs. But the tasks required to achieve these results are complex. At the highest level, the basic building blocks of a data analytics platform are:

The purpose of a data platform is to enable, store, process and provide data for analysis while supporting the appropriate level of organizational management. To do this while providing a functional, modular, scalable, scalable, scalable and cost-effective platform, data platforms require a layered architecture. These layers are functional elements that perform specific tasks in the data lake system

Designing Cloud Data Platforms

In practical terms, a layer is a cloud service, an open source or commercial tool, or another application component that you deploy yourself. Usually it is one of several such components, but where possible, we recommend using a Platform as a Service (PaaS) solution to reduce support requirements.

Netapp Spots A Data Platform Opportunity In The Cloud

In terms of software development, these functional layers must be interconnected. This means that the different layers must communicate with a well-managed interface, but must rely on the internal functionality of a particular layer. This approach is important because it gives you the confidence to mix and match different cloud services and/or other tools to achieve your goals.

Cloud applications are famous for constant change because of the constant and seemingly endless new services or service improvements released by cloud vendors, or new projects from the open source community. This is good news. It means that the wealth of features is always growing. But changing services can be a challenge – unless, of course, your data platform is not architecturally stable, so the impact of changes can be small in the future and separate from other floors. This layered approach allows you to respond to changes and updates with the least possible impact on the overall platform structure.

Let’s explore each of these high-level layers. Then we’ll match each layer with the Google services you use to bring your design to life.

Storage level about accessing data in the data lake It connects to, and retrieves data from, various data sources such as relational or NoSQL databases, file storage, and internal or third-party APIs. However, the proliferation of different data sources from which organizations now feed their analytics means that this level is extremely flexible.

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Therefore, the transformation layer is often implemented with a variety of open source or commercial tools, each of which is specialized for a specific type of data source: An important feature of a transformation layer of a data platform is that it should not change or transform the incoming data . Either way it ensures that raw, processed data is always available on the platform for data line tracking and processing. Cloud services like Google Cloud also provide data loss prevention (DLP) APIs, which help clean raw data that may contain PII, thereby making raw data accessible for analytical use cases/ machine learning but safely leaves no data. entering PII

After the data is stored in the cloud storage in its original form, it can now be stored using a compression or extraction process to make it useful. This data processing is the most interesting part of a data platform. While data lakes make it possible to perform analysis directly on raw data, this is usually not the most productive approach: usually, the data is slightly modified so that it can be used by analysts, data scientists, or be more useful to other users of the data. Data processing in a data lake involves many different steps, including schema management, data validation, data cleansing, and generating data products.

With this in mind, a data processing framework that can scale infinitely and with cloud computing resources that you can tap into at any time are key components of a modern data platform. Over the past few years, several data processing frameworks have been developed that enable scalability, support modern programming languages, and fit well into the public cloud paradigm. The most notable are Apache Spark, Apache Beam and Apache Flink.

Designing Cloud Data Platforms

At a high level, all of the above frameworks allow you to write data cleaning, transformation, or validation functions using modern programming languages ​​(usually Java, Scala, or Python). These frameworks then read data from distributed cloud storage, divide it into smaller chunks as the data volume requires, and process those chunks using flexible cloud computing resources.

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When thinking about the data processing layer in a data platform, we must also keep diversity in mind.

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