3 reasons you should migrate on-prem Spark workloads running on Hadoop to Azure Databricks
Hadoop has been a popular software framework for the storage and processing of large sets of data. This has led to many enterprises and SMB organizations leveraging Hadoop at scale.
With the advent of cloud computing, however, applications are now being built in the cloud, far away from on-premises databases, resulting in challenges for Hadoop users.
In this blog, we discuss how migrating on-prem Hadoop can help resolve challenges centered around:
- Data gravity and real-time data access
- Inefficiencies caused by manual workflows
- Ensuring data consistency and cleanliness
Why you should migrate on-prem Hadoop
With many organizations striving to become cloud-native, more apps are being built in the cloud. As a result, those with large Hadoop deployments are discovering significant networking challenges integrating cloud apps with on-premises Hadoop.
1) Fight data gravity
At the core of the challenge is data gravity or the concept that apps and services typically designed to be as “close” as possible to data sources. With apps running in the cloud and data stored and processed in on-premises Hadoop, enabling the connections between data and the apps requires the creation of additional workflows, creating security concerns and latency.
The additional workflows prevent the cloud apps from being able to gain instant access to data that they typically expect, creating inefficiencies and increasing the likelihood of reduced performance.
2) Avoid workflow inefficiencies
Related to the issue of data gravity, the workflows that must be implemented to support the use of Hadoop in conjunction with cloud apps create another set of challenges. These workflows are slower than direct and secure connections between cloud resources, and they often require manual intervention. The lag can create a disparity between the data in the Hadoop environment and the data a given cloud app is leveraging in the cloud.
For databases with constantly changing datasets, this can create an additional layer of complexity as organizations will need their workflows to constantly update the data available to the cloud environment.
3) Data consistency
Data lakes, whether it be an on-premises Hadoop-based data lake or a data lake in the cloud, are frequently prone to data consistency issues. This is because they are typically connected to several disparate data sources with data pipelines constantly reading and writing data.
While data consistency challenges can be mitigated through thorough data integration and data pipeline construction practices, ensuring data integrity frequently remains a slow, tedious process.
Azure Databricks, the perfect cloud database for Spark workloads running on Hadoop
Since Databricks was created by the original developers of Apache Spark and MLflow, it can provide organizations with the perfect solution for the above challenges. By being a cloud-native database, the challenges surrounding data gravity and workflow inefficiencies are replaced with APIs and cloud-enabled features that allow organizations to easily integrate apps and services, plus begin taking advantage of machine learning and data science.
Eliminating the extra workflows also allows organizations to improve data consistency by eliminating much of the complexity from their data orchestration. Further, because Databricks runs in the cloud, it is simple to leverage cloud services such as Microsoft’s Common Data Service to ensure data is kept clean and consistent.
Why Azure Databricks? Why not AWS?
Databricks can run on any of the major cloud providers, including both Azure and AWS. This makes it a great choice for any organization looking to migrate their Hadoop workloads to the cloud.
Azure Databricks, however, takes the performance boost a step further by leveraging a highly optimized version of Spark. This optimized version of Spark enables an additional performance boost of up to 50x, meaning Azure Databricks users stand to gain a whopping 5,000x boost in performance by migrating their on-premises Hadoop workloads to Azure Databricks.