The architectural anatomy of data warehouses and an insight into various stages of Netezza migration


The data warehouse is one of the most critical components of a business. It performs the dual functions of data collection and business report generation for the effective functioning of a business. When a business decides to upscale its operations or carry out migration, the data warehouse has a pivotal role in this process. The aim of this article is to analyze the process of effective Netezza migration.

The preparation stage

There are a large number of organizations that make use of data warehouse systems. These systems serve as a storehouse of data and enable the execution of effective data analytics in the long run. Even if the data is heterogeneous, analytics can be carried out with the help of modern intelligent systems and applications. In addition to this, systems are equipped with modern computing resources which help in dealing with a large number of clients in a customized manner. After the preparation stage is over, the next stage involves the identification of workloads and other types of dependencies.

The identification stage

It is important to understand and identify the various types of workloads that are present in the data warehouses. The first among them is called the batch processes. The batch processes are processes that require a large number of resources and need to be maintained for a prolonged time. The second type of workloads are called Adhoc queries. Adhoc queries are relatively short queries but have high concurrency. They have an effective role in data analytics. The third type of workloads are referred to as the business workloads. The business workloads are usually hybrid workloads that include various business analytics applications.

With the help of Netezza migration, the clients are not only able to fulfill the various requirements of workloads but are also able to retain critical components of the whole data warehouse. The migration process also allows the customers to split the data warehouse into two different components or clusters so that all the workloads are served independently if the need arises.

Primary and secondary cluster

The primary cluster is responsible for holding the core schemas and most important data. This data also serves as the core requirement when it comes to processing different business applications. The primary cluster is also able to manage different storage requirements and other batch processes in a comprehensive way. The purpose of the secondary cluster is to serve a single application that demands input and output facilities.

The planning stage

There are a lot of approaches when we talk about database migration. The prime requirement of all is to have as minimum downtime as possible. The challenge that we encounter while database migration is the capturing of changes that we may encounter in data systems. Another important challenge is to keep the data updated during the process of migration. That said, the process of planning usually takes place in the following steps. The first step in the planning stage is the analysis of data sets. This is followed by the bulk point-in-time export. This step is also called the transportation step which includes the validation of data. During this step, business activities function as normal on the current system. After this, the auditing process takes place as per the specific applications. Before the migration of data, we may carry out the technical validation of future systems.

Carrying outmigration

Before the full-fledged process of migration is carried out, small trial runs are conducted. Two important requirements need to be taken care of before carrying out full data migration. The first important requirement is to mitigate the impact on the data source. The second important requirement is to decrease the latency for efficient and effective transfer of data. The third important requirement is to monitor the network performance when the migration process is going on.

The validation requirements

There are a number of activities that are validated during this phase. The first among these activities is to identify the skewed table data. Any anomalies in the table data are rectified at this stage. Whenever incorrect column encodings are encountered, they are rectified. It is at this stage we ensure that the processes are devoid of any crashes during the migration process. The final step in this process is the validation of job duration and other tasks like the number of rows and columns processed in a specific duration.

Concluding remarks

This article highlights the important takeaways that are extremely important when it comes to the large-scale migration process. Key takeaways can not only benefit small startups but can also be handy for tech giants if they plan for a timely migration process.