Accelerate your migration journey with Neal’s SQL migration automation tool

The need for cloud migration

It’s no secret that the cloud is critical in any given organization’s digital transformation today. The cloud allows organizations to leverage cost-effective, on-demand computing and storage resources. It will enable them to gain more from their apps and data by giving them access to tooling and solutions for advanced analytics, AI, and more.

According to Economic Times, more than 51% of respondents claim that 100% of their infrastructure is now in the cloud. And 49% said they plan to move more workloads into the cloud.

The need for automated migration

Cloud migration can bring many benefits, but it’s not without challenges. Gartner has highlighted that organizations must consider parameters like how well applications perform in a new environment, bandwidth restrictions, latency problems, and domain management. Proper planning, testing, and automation during the migration process can help organizations address these issues and ensure a smooth and faster migration.

To help simplify the migration process, Neal Analytics has developed an automated SQL migration tool that enables organizations to accelerate migrations to spark-based platforms like Azure Databricks.

Neal’s automated SQL migration tool

Neal’s automated SQL migration tool can accelerate an organization’s journey to the cloud. The tool can bring more than 50% automation into code conversion from Netezza/Oracle/Terra to Azure Databricks using PySpark and SparkSQL.

The Automated Migration tool can help accelerate migration in the following ways:

Reducing manual effort and migration time
Reducing migration costs and lowering the risk
Reducing business disruption
Increasing productivity
Achieving greater resiliency

How do we achieve it?

Neal’s Automated Migration tool converts Native/Netezza SQL to PySpark and SparkSQL. Based on the code complexity level, we can convert the code either in PySpark (Simple and Medium) or SparkSQL (Complex). It splits the command based on the different functions using key-value pairs and converts the SQL command into PySpark syntax. Once we convert the query to PySpark syntax, we can use it in the data frames to run the code in Databricks using the PySpark and SparkSQL engines.

Why Databricks? Why PySpark?

Using a cloud-based platform like Azure Databricks, which uses PySpark, can help organizations gain faster time to value and increase accuracy with their data, allowing them to make more informed business decisions. The PySpark framework is a preference as it processes large amounts of data more quickly than conventional frameworks. Organizations prefer it as it helps in performance enhancement, scalability, productivity, integration, and security faster and more consistently.

PySpark, in specific, can help resolve several challenges that are commonly faced when working with big data:

Data Processing: PySpark provides a powerful, high-performance that can process data faster and more efficiently, which can be challenging to handle with traditional frameworks.
Data Integration: PySpark allows easy ingestion of data from different sources, such as flat files, databases, and streaming data, and integrates it for analysis. With Azure Databricks PySpark, you can easily integrate it with other Azure services like Event hub, Data factory, and Data Lake, allowing you to access and process data from various locations.
Data Analysis: PySpark includes a rich set of libraries and tools, such as SQL and DataFrames, that make it easy to perform common data analysis tasks, such as filtering, aggregation, and join operations.
Machine Learning: PySpark’s ML library provides a robust set of tools for performing machine learning tasks, such as classification, clustering, and regression, which help analyze large datasets and build predictive models.

Accelerate your migration today

Neal Analytics leverages a flexible engagement model designed to enable our customers to engage with us the way that’s best for them. If you would like to learn more about using the automated SQL migration tool to accelerate your move to Databricks, contact us and one of our migration experts will reach out to you.

automation Databricks Migration SQL Server