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How Vendor Lock-in Works In Analytics And How To Avoid It

How Vendor Lock-in Works In Analytics And How To Avoid It

Vishal Chawla
vendor lock in analytics
W3Schools

Despite all of the valuable technology products available today on the cloud, many corporations who are considering migrating to the cloud have concerns. And one of the primary issues is vendor lock-in. 

For starters, vendor lock-in means a condition where the price of switching to another vendor is so high that the customer is stuck with the first vendor. Because of budgetary pressures, an inadequate workforce, or the necessity to avoid obstructions to business operations, the customer is locked-in to what may be a substandard product or service.

Challenges That Can Arise Due To Vendor Lock-in

Today, many companies have no dedicated servers or dedicated capacity and are priced according to the compute capacity consumed by them. In theory, cloud workloads can be moved from one public cloud provider to another, but it would be a complicated task by embedding a company into a single public cloud infrastructure limits the ability to change vendors. 



If a vendor’s quality of service declines, or never meets the desired threshold, to begin with, the client will be stuck with it. The vendor may also drastically change their product offerings in such a way that they no longer meet business needs. Finally, a vendor may impose massive price increases for the service, knowing that their clients are locked in.

Vendor lock-in that comes in with cloud platforms like AWS, Google and Microsoft, among others, which provide machine-learning-as-a-service (MLaaS) can act as a roadblock to some enterprises. 

Choosing the correct vendor or cloud-based analytics can help companies to avoid massive costs and gain benefits in the long run. 

The fact is that vendors can make decisions that are in line with the goals of their customers, especially those companies that are small or use cloud products in very niche applications and use cases. But public cloud vendors (Amazon, Microsoft, and Google) are so huge that it is quite unlikely they will make decisions that negatively impact a large number of users. 

The Multi-Cloud Has Made Vendor Lock-ins A Big Concern

Companies are always worried about the safe and secure portability of data and workloads across different cloud environments. Today open-source data solutions such as MongoDB, Apache Hadoop, Apache Kafka, etc. have emerged to give a great answer to the big data problem and helped companies escape the draconian pricing of traditional cloud vendor lock-ins. 

Let’s say if a company has an entire data lake and analytics solution on a public cloud platform for a particular region and needs to deploy that solution to another cloud. Implementing this solution on a separate cloud platform would demand a lot of re-work. Therefore, a large number of companies are now exploring flexibility to implement their solutions on any of the widely accessible public or private cloud platforms.

But despite many of the platforms having APIs and open-source connectivity, frameworks are generally inflexible. For example, data scientists can use AWS Sagemaker as the tool to use for training and deploying models. Sagemaker promises to cut in training and deployment time by handling all the infrastructure but runs exclusively on AWS. This may not be a problem if you run your business on AWS, but if new products or tools are offered on other public clouds, there is no way to access them. 

There are also many companies, particularly startups which provide solutions for streamlining operations across various servers, cloud and containers, by providing a transparent platform built to work across multiple clouds. For instance, Cloud Foundry, a container-based architecture that runs apps in any programming language, helps users deploy, and manage high-availability Kubernetes clusters with its open-source project BOSH on any cloud. The project can help decouple applications from infrastructure, so users can host workloads – on-premise, in public clouds, or in managed infrastructures. 

Cloud giants too, have introduced solutions that may help escape vendor lock-ins. For example, by using Anthos from Google, companies can manage their cloud data workloads across multi-cloud, so that a particular cloud provider does not restrict developers and data scientists. 

See Also

Proprietary Vs Open Source Data Analytics Products

Customers today have alternatives to proprietary tools with advances in open source software technologies, along with a range of ‘as-a-service’ capabilities that can remake traditional IT. Projects like Apache Kafka, Apache Spark and Kubernetes are widely accessible as a service on the large cloud platforms. This convergence of open source and proprietary platforms is one of the ways to avoid vendor lock-in in today’s era. 

Open source is the king. It is perfectly fine to augment whatever you are doing with proprietary tools, but do not be too dependent on them and do learn how to do it without relying on a single vendor. 

In a recent survey by Anaconda, it found that developers and data scientists value open source so they can get work done right away. It also suggested that many responders believe open source helps prevent vendor lock-ins in data science. An open cloud architecture helps prevent vendor lock-ins and makes it simpler to operate with various analytics services. 

With the fight over cloud becoming intense, and users trying to avoid the situation of cloud lock-ins, cloud companies themselves are increasingly turning to open-source container technologies and providing them via managed containers. Growth and acceptance of containers have a positive impact on big data analytics and vice versa as they can process and manage vast amounts of data from disparate sources on the cloud via managed containers.  

In recent years, Kubernetes has appeared as a gold-standard of implementing cloud-native yet cloud-agnostic solutions. It is paving the way for innovation across the cloud infrastructure domain. Containers and microservices make the development process simple and render other benefits, like decreasing the complexity of running and updating apps and advancing the consistency linking testing and production environments. 

What To Do To Prevent Vendor Lock-In?

  • Companies using cloud computing should make an effort to keep their data portable or easy to move from one environment to another. They can partially do this by clearly defining their data models and keeping data in formats that are usable across a variety of platforms, rather than formats that are specific to a given vendor. 
  • Data should be stored in open source formats in the cloud, not in proprietary formats in a vendor’s software or cloud platform. There should be flexibility to choose any existing and future technologies to access, process and query data. For example, you could use Amazon S3 to store the data, Databricks to process it, and Tableau and Power BI visualise it.
  • Keeping internal backups of all data helps a business stay ready to host the data elsewhere if it is too difficult to extract it from cloud service. It also provides protection from ransomware.
  • Companies can prevent vendor lock-in by opting for an enterprise AI platform which gives them a smooth cloud integration with all of their preferred cloud hosting providers. One such platform is DataRobot which supports all of the cloud hosting providers and enables enterprises to scale their data science infrastructure securely and cost-effectively.
  • Using open-source such as sci-kit-learn, Tensorflow, etc. is more effective in creating a model that is full-featured and is more suited to the data scientist’s workflow. 
  • With containers, applications are portable and ready to be deployed on any platform. It is also cheaper. In complex applications, Kubernetes give more control in the hands of the developer teams, empowering them to build with ease. It can give teams the flexibility to move to any public/private/hybrid cloud solution as per their needs.
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