Cloud Data Platforms

Cloud Data Platforms – 5 Cloud Data Platforms, 33 Dimensions, 400+ Hours of Detailed Analysis — 1 Incredible Cloud Data Platform Benchmark Analysis.

It’s hard to do an apples-to-apples comparison of the best cloud data platforms available on the market today, but three of our data cloud experts were up for the challenge. Chinmayee Lakkad, David Hrncir, and George Luft joined Kelly Kohlleffel at Hashmap on Tap to discuss how they spent over 400 hours benchmarking Snowflake, AWS Redshift, Azure Synapse, Google BigQuery, and Databricks using industry standard performance metadata -DC. , loading rates, conversion rate and total cost in 33 dimensions to provide an unbiased perspective.

Cloud Data Platforms

Cloud Data Platforms

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Cloud Data Platforms

Innovative technologists and domain experts who help accelerate the value of data, Cloud, IIoT/IoT and AI/ML for our community and customers by creating intelligent, flexible and high-value solutions and service offerings that work across industries.

Everest Group Reports

Innovative technologists and domain experts accelerating the value of data, Cloud, IIoT/IoT and AI/ML for our community and customers A data platform is an integrated set of technologies that collectively fulfill an organization’s end-to-end. data needs. It enables the capture, storage, preparation, distribution and management of your data, as well as a layer of security for users and applications. A data platform is the key to unlocking the value of your data.

But data platforms can be a complex subject. What exactly is behind a data platform? How do you approach design? And what is the difference between a customer data platform, a big data platform and an operational data platform?

Over the past 20 years, IT vendors have tried to develop and offer solutions to deal with the flood of data that companies face from inside and outside the business.

Cloud is the new norm and cloud native data warehouses are now massively parallelized. Data pipelines can handle terabytes of data. Storage has become cheap and fast, and processing frameworks like Spark can handle large volumes of data. NoSQL augments relational databases and Graph augments traditional languages ​​like SQL while AI/ML applications are ubiquitous.

The Building Blocks Of A Modern Data Platform

Although these individual pieces of technology have matured, most businesses have not been able to integrate these tools. The result is data silos that are often unscalable, contain duplicate, often outdated data, locked in proprietary solutions and lack a single layer of security.

A modern data platform tries to solve this problem. It is a combination of interoperable, scalable and replaceable technologies that work together to deliver the overall data needs of an enterprise.

People often refer to data platforms by different names. Sometimes these names mean the same thing. Sometimes they relate to the different types of data they hold and the type of workload they process. To make matters even more complicated, there is overlap between some of their use cases.

Cloud Data Platforms

Building a modern data platform requires adopting a modern data architecture (MDA) that specifies how data will be collected, cleaned, stored, transformed, processed and made available to consumers. A modern data architecture has the following characteristics:

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End users are at the center of modern data platform architecture. Rather than being limited to a set of pre-developed data assets and their sources, users can bring their own data to the platform and develop their own pipeline to ingest, clean, analyze and report on that data.

The modern data platform encompasses the best of the on-premises and cloud worlds. On-premise ensures minimal changes to legacy applications and the cloud provides scalable and elastic capacity, processing power, high availability, pre-built applications and security.

At the core of a modern data platform is the virtual data storage layer, which can handle different data formats and workloads. For example, the platform can support different data storage formats for functional/transactional databases that support real-time interactions, data lakes containing unstructured data, and data warehouses needed for groups of structured data needed for popular analytical jobs.

Therefore, the storage layer is more of an “abstraction” over the other components of the platform. At a low level, users and applications will access it using a common set of protocols and standards such as REST APIs. From a user perspective, this data will be transparently federated and virtualized, allowing users to share and collaborate on it.

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Ingestion, validation, cleansing and preparation are key to a data platform. A flexible data architecture uses scalable pipelines that can handle different scenarios: batch ingestion from legacy sources using APIs, pub/sub for asynchronous event messaging, and stream processing for high-speed real-time data.

The processing architecture of a modern data platform allows the development and reuse of service-oriented applications. These applications serve domain-specific functions and are often based on open source technology. In the most advanced cases, the platform can also allow the development of next-generation applications based on AI and ML logic in various workspaces.

Data is automatically classified and tagged in a data platform. This metadata powers a comprehensive data catalog that users can search for self-service data discovery. The management model also allows users to control data quality and sensitivity. Finally, data pipeline reporting can show the journey of a data element through the system at any given time.

Cloud Data Platforms

The analytics layer allows the development, distribution and sharing of self-service dashboards, reports and notebooks based on flexible technology. Organizations can leverage their existing analytics applications using various integration libraries.

Tetra Data Platform

The first category ensures that all physical elements of the platform such as servers, backups, storage and load balancers can be easily recreated from scratch if necessary.

The second type of automation ensures that data pipelines, workspaces, notebooks and functions are created from standard templates whenever a new data source is entered.

Finally, the security layer of a modern data architecture abstracts the access mechanisms of individual applications. It can use an enterprise-wide Identity Provider (IdP) for authentication and role-based authorizations for access. A solid data architecture also ensures that data is protected while meeting regulatory standards.

Building a modern data platform needs the right data strategy. Although it’s a big topic in itself, here’s a five-point introduction.

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The types of data platform we’ve talked about so far are mostly about aggregating data from various sources and using that aggregated data to answer business analytics questions.

Another type of data platform deals with high-volume operational data used for application development. These “operational” and application data platforms are increasingly hosted in the cloud for scalability and ease of use, have built-in high availability and disaster recovery, provide robust data security at rest and in transit and allow workload isolation, performance monitoring and alerting.

One such platform is Atlas. Atlas is a database as a service (DBaaS) that enables organizations to create cloud clusters – without worrying about infrastructure provisioning, instrumentation, scale, performance monitoring, high availability, security, backups, disaster recovery or database management.

Cloud Data Platforms

Atlas can work seamlessly with other data platforms to enhance their capabilities. For example, it can natively run federated queries across AWS S3 and Atlas clusters. Allows the combination of operational data and historical object storage data in virtual databases and collections in Atlas Data Lake.

How We Help Our Clients To Improve Analytics Thanks To A Modern Data Platform?

Data platforms are key to understanding, managing and accessing your organization’s data. In the end, it depends on what you want to do with your data and how you want to do it. Whether you’re building a customer data platform or using an operational data platform like Atlas, data platforms can unlock the potential and revenue your data has been hiding.

There are many services or functions that tie together the components of a data platform. Examples could be data management service, Data Quality Service (DQS), Master Data Management (MDM) service, broadcast service, message bus, authentication service, etc.

It really depends on the perspective of the user. You can build your own big data platform using applications built by

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