What are the Benefits of a Managed Service?

Managed Services – Understanding the basics

What are the operational benefits of using a managed service for Presto with Ahana Cloud? To answer this question, first let’s hear from an AWS Solution Architect about his experience using Ahana as a solution for his data lakehouse: “Ahana Cloud uses the best practices of both a SaaS provider and somebody who would build it themselves on-premises. So, the advantage with the Ahana Cloud is that Ahana is really doing all the heavy lifting, and really making it a fully managed service. The customer of Ahana does not have to do a lot of work. Everything is spun up through cloud formation scripts that uses Amazon EKS, which is our Kubernetes Container Service.”

The architect goes on to state, “the customer really doesn’t have to worry about that. It’s all under the covers that runs in the background. There’s no active management required of Kubernetes or EKS. And then everything is deployed within your VPC. So the VPC is the logical and the security boundary within your account. And you can control all the egress and ingress into that VPC.”

In addition to this the AWS architect continues to state, “this is beneficial. As the user, you have full control and the biggest advantage is that you’re not moving your data. So unlike some SaaS partners, where you’re required to push that data or cache that data on their side in their account, with the Ahana Cloud, your data never leaves your account, so your data remains local to your location. Now, obviously, with federated queries, you can also query data that’s outside of AWS. But for data that resides on AWS, you don’t have to push that to your SaaS provider.”

Now that you have that some context from a current user and a solution provided from this data architect, let’s get more specific about the reasons a user would want to select a managed service for their SQL engine for Data Lakehouse analytics and reporting.

For example, let’s say you want to create a a new cluster. It’s just a couple of clicks with Ahana Cloud, rather than an entire arduous process without the faciliation of a service. You can pick the the coordinator instance type and the Hive metastore instance type. And it is all flexible.

In this scenario, as to further progress with this illustration, instead of using the Ahana Cloud provided Hive metastore, you can bring your own Amazon Glue catalog. This allows the user to main control and streamline their tasks.

Then of course it’s easy to add additional data sources. For that, you can add in JDBC endpoints for your databases. Ahana has those integrated. After the connection, then Ahana Cloud automatically restarts the cluster.

When compared to EMR or with other distributions, this is more cumbersome for the user. All of this has to be manually completed by the user when they are not using a managed service:

• You have to create a catalog properties file for each data source
• Restart the cluster on your own
• Scale the cluster manually
• Add your own query logs and statistic
• Rebuild everything when you stop and restart clusters

With Ahana Cloud as a managed service for PrestoDB, all of this manual action and complexity is taken away, which in turn is allowing the data analysts and users to focus on their work – rather than spending a large amount of time being distracted with high labor processes and complicated configurations as a prerequisite to getting started with analytical tasks.

For scaling up, if you want to grow the analytics jobs over time, you can add nodes seamlessly. Ahana Cloud, as a managed service, and other distributions can add the nodes to the cluster while your services are still up and running. But the part that isn’t seamless or as simple, like with Ahana, is when you stop the entire cluster.

In addition to all the workers and the coordinator being provisioned, the configuration and the cluster connections to the data sources, and the Hive metastore are all maintained with Ahana Cloud. When you as tne user restart the cluster back up, all will come up pre-integrated with the click of a button. Meaning, the nodes get provisioned again, and you have access to that same cluster to continue your analytics service. T

Here this is noted as rather important. The reason for this is because otherwise the operator would have to manage it on your own, including the configuration management and reconfiguration of the catalog services. Specifically for EMR, for example, when you terminate a cluster, you lose track of that cluster altogether. You have to start from scratch and reintegrate the whole system.

Next Steps – Exploring a Managed Service

As you are your team members are looking to reduce the friction from your data analytics stack, learn how Ahana Cloud reduces frustration and time spent configuring for data teams. The number one reason for selecting a managed service is that it will make your life easier. Check out our customer stories to see how organizations like Blinkit, Carbon, and Adroitts were able to increase price-performance and bring control back to their data teams – all while simplifying their processes and bringing a sense of ease to their in-house data management outfits.

Related Articles

A Comprehensive Guide to Data Warehouse Types

A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. Learn more about what these data warehouse types are and the benefits they provide to data analytics teams within organizations..

Presto vs Snowflake: Data Warehousing Comparisons

Presto is an open-source SQL query engine, developed by Facebook, for large-scale data lakehouse analytics. Snowflake is a cloud data warehouse that offers a cloud-based information storage and analytics service. Learn more about the differences between Presto and Snowflake in this article.