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Netezza was introduced in 2003 and became the first data warehouse appliance in the world. Subsequently, there were many “firsts” too – 100 TB data warehouse appliance in 2006 and petabyte data warehouse appliance in 2009.

Netezza has had an amazing run, with unmatched performance due to its hardware acceleration process in field-programmable gate arrays (FPGA). This could be fine-tuned to process intricate queries at blistering speed and scale. Data compression, data pruning, and row-column conversion were all handled optimally by FPGA.

During the lifetime of Netezza, various versions have been launched and all of them have provided high value to the users with simplified management, data pruning, and no need for indexing and partitioning of data. Then, why would users want to migrate data from Netezza to Snowflake?

The cloud-based data warehouse revolution made a huge difference to Netezza as IBM withdrew support. New hardware has not been released since 2014. By doing so IBM has forced Netezza users to abandon the appliance and opt for cloud-based data warehousing solution Snowflake.

There are many benefits of Snowflake for those wanting to shift from Netezza to Snowflake. Snowflake is a premium product, providing a great deal of performance, scalability, and resilience, more than other cloud-based data warehouse solutions.  

Additionally, there are many advantages of shifting to the cloud and Snowflake for data management.

·        Affordable – Enterprises do not have to invest in additional hardware and software. This is very critical for small industries and start-ups. In this pricing model, users can scale up or down in computing and storage facilities and pay only for the quantum of resources used.

·        Reliability – Reliability and uptime of server availability are mostly in excess of 99.9%.

·        Deployment speed – Organizations have the leeway to develop and deploy applications almost instantly because of access to unlimited computing and storage facilities.

·        Economies of scale – When several organizations share the same cloud resources the costs are amortized for each of them, leading to economies of scale.

·        Disaster recovery – When there is an outage in primary databases, the secondary databases in the region are automatically triggered and users can work as usual. When the outage is resolved, the primary databases are restored and updated automatically.          

There are two steps in any Netezza to Snowflake data migration strategy.

The first is the lift-and-shift strategy which is used when there is timescale pressures to move away from Netezza with the need to move highly integrated data across existing data warehouse. This is also relevant when a single standalone and independent data mart has to be migrated.

The second is the staged approach. This is applicable when many independent data marts have to be moved independently. The focus here is on new development rather than reworking legacy processes.

Do you find that your Oracle server is too slow for analytical and intricate queries? Do you feel that you are draining precious resources to increase storage capacity or compute power of your Oracle instance? Is there a need to combine data seamlessly from multiple databases? If your answer to one or more is a YES, you should explore the amazing solutions that Amazon S3 has to offer in these cases.

However, before moving database Oracle to S3, a quick preview of the two will be in order.

Oracle database

Oracle is a multi-model, proprietary, relational database system primarily used for data warehousing and OLTP (Online Transaction Processing). The recent versions though have features like columnar storage and on-cloud deployment that are similar to Amazon S3 (Simple Storage Services).

Amazon S3

Amazon S3 offers an unmatched feature-rich storage platform. It ranges from basic storage repository for backup and recovery to primary storage for the most advanced and intricate applications available in the market today. You can either use tools like Amazon Athena or Redshift Spectrum to query directly or leverage the power of cutting-edge technologies like Spark or EMR over S3 for AI or ML workloads.

Amazon S3 is a cloud-based database optimized for Online Analytical Processing (OLAP) and data warehousing. You can launch applications on it quickly in just two steps. First, launch an S3 cluster through simple API calls or through the AWS Management Console. Second, connect the local SQL client to this S3 instance.

Because of its many advantages and unique architecture deciding to move data from database Oracle to S3 is the best way to take your data infrastructure to the next level.

Preferred Tools to move Oracle database to Amazon S3

For this process to be successful, a script or sometimes a combination of scripts has to be written. But it is often a tedious task. The incremental load is especially tricky to code and execute in a manner that will make sure that there is no data inconsistencies or data loss. With organizations wanting to look at real-time data and this execution being done more than once in 24 hours, a robust and effective tool for migration is compulsorily required.

Choose a tool that will shift data from Oracle to S3 in minutes without any implementation time. The best tool can complete the process in three steps.

·        Connect and configure the Oracle database.

·        Choose the replication mode. Use a custom SQL query to select the Oracle database tables to be loaded via interface load data.

·        Configure the Amazon S3 Data Warehouse for data load.    

The most optimized tool has a user-friendly interface that allows even the non-technical people to fine-tune the data load settings. It will also have a fault-tolerant architecture to prevent data loss during loading.

There has been a raging controversy in recent times. It is over retention of Oracle database in internal operations in organizations or switching to Amazon S3 (Simple Storage Services) of Amazon Web Services (AWS) or their other options.

Before going into the benefits of migrating databases from Oracle to S3, it is useful to know what each has to offer.

Oracle Database

Oracle databases have been an integral IT infrastructure part of the world’s leading businesses for decades now. It was one of the first to support GNU/LINUX and Windows Server and can be run on a broad range of hardware and software as its exclusive stacking feature enables all applications to be easily merged into its database. Further, data reliability and integrity is never at risk as Oracle is ACID-compliant. For long, there had been no truly effective alternative for Oracle database until cloud computing and AWS arrived on the scene.

Amazon Simple Storage Service (Amazon S3)

This is an object storage service in the cloud that provides unmatched data availability, scalability, performance, and security. Organizations, regardless of the size or scale can store and protect any volume of data from websites or mobile applications. S3 can also be used to backup, restore, and archive enterprise applications, big data analytics, and IoT devices. S3 has user-friendly management features. Organizations can quickly categorize, configure, and fine-tune access controls to meet specific business and compliance needs. Most critically, Amazon S3 offers 99.999999999% (11 9s) of durability and can store almost unlimited applications.

There are several ways in which AWS enables Oracle databases to be more efficient, which is one reason to migrate databases from Oracle to S3. There is a reduction in payouts to Oracle and many even opt to have the Oracle database replaced entirely with native Amazon services. Businesses though have to first figure out how to move the data and to which service before migrating databases from Oracle to S3 AWS.

Here are some methods that will fit into the IT infrastructure of enterprises. There are primarily three choices for Oracle users on AWS.

·        EC2 – EC2 compute instances and EBS (Elastic Block Store) storage can be used to run on-premises Oracle databases and replace the servers and storage with AWS infrastructure.

·        RDC – Businesses can shift to Amazon Relational Database Service. It is a managed database service for Oracle and other popular databases, reducing many management tasks while still operating on the AWS infrastructure.  

·        Aurora – Amazon Aurora is a database platform that is designed and optimized for its cloud infrastructure. It is not a precise replacement for Oracle but includes most of its critical features and is the platform to which Amazon itself is migrating.

Oracle database has been around for decades now, being commercially launched first in 1991. Since then, it has met the database requirements of organizations and led the data landscape. Oracle has many unique features and one of the most important is that it runs on a wide spectrum of hardware and software operating systems. Hence, the database enables swift and efficient data storage and retrieval facilities. Oracle database was the first to have supported GNU/LINUX and being ACID-compliant makes ensures data reliability and integrity.

Where then is the need to replicate data Oracle to Snowflake?

The answer lies in the exclusive benefits that Snowflake provides which are especially useful for the modern-day data-based business environment. It is fully cloud-based and is offered as a Software-as-a-Service product. As it operates exclusively in the cloud, it is not dependant on any “big data” software, and businesses can quickly start operating on the platform without any additional investments in hardware or software.

All maintenance and tuning issues are directly taken care of. Snowflake uses a new SQL database engine that has a range of functionalities and capabilities, more advanced and user-friendly when compared with traditional databases.

A critical reason why businesses want to replicate data Oracle to Snowflake is the stringent data security and safety provided by the platform. Snowflake automatically encrypts data with federated and multi-factor authentication to ensure that there is no loss or breach of data. This data warehousing solution follows the stringent security standards as laid down by HIPAA regarding activities and data objects within the database.  

The process to replicate data Oracle to Snowflake is facilitated by certain in-built features. The Standard version ensures that data is shared securely across clouds and regions. It enhances smooth data portability and helps to replicate or load databases from others like Oracle or Microsoft SQL Server. This is a new addition to Snowflake. The older Enterprise for Sensitive Data (ESD) version has a Database Failover and Failback feature that is extremely useful for businesses whenever an outage or disaster strikes.

What is this ESD model and why is it so important for enterprises around the world that depend on “big data” to smoothly run their businesses? In the event of an outage, the Database Failover and Fallback feature immediately set in motion a fallback and failover activity for seamless data recovery. Secondary databases existing in the region are triggered automatically which in turn starts acting as temporary primary databases to take up the load.

Once this outage is resolved, the failover and failback process starts working in the reverse direction. The primary databases are refreshed with the changes and new data generated during the outage and normal workloads can be resumed. This whole cycle does not need human intervention at any level.

When you have a system in place that has served your data retrieval and storage needs for long, why would you want to switch to another comparatively new solution?

Microsoft SQL Server is one that has for long met most SME requirements and workloads. It supports most applications across the web or on a local network system on a single machine and blends seamlessly with the full Microsoft ecosystem. Then, why do many organizations today want to replicate data SQL Server to Snowflake?

Snowflake has an advantage if you have big data needs but not much for relatively small datasets or low currency/load. This cloud-based data warehousing solution has almost unlimited storage capacity, a friendly user-interface, and very stringent data control and security measures. All these attributes are expected from a data warehouse but not available fully in Microsoft SQL Server.

There are other benefits to Snowflake. It offers separate computing and storage facilities. Users can scale up or down in either of them, paying only for the resources used. Further, multiple users can work simultaneously on intricate queries and workloads without experiencing any lag or drop in performance.

Basically, Microsoft’s SQL Server is a database server with primary functions being to store and retrieve data. It is a combination of the Relational Database Management System (RDBMS) and the Structured Query Language (SQL). Many specialized versions released recently though cater to a wide range of workloads and demands.  

Snowflake, on the other hand, is cloud-based and offered as a Software-as-a-Service product. It runs on AWS, the most popular and commonly used cloud provider. As in other databases, it is possible to query any structured data in Snowflake tables through the standard SQL data types such as NUMBER, BOOLEAN, VARCHAR, TIMESTAMPS, and more. These features are added incentives to replicate data SQL Server to Snowflake.

Tools to replicate data SQL Server to Snowflake

The process of replicating data to Snowflake need not be a long-drawn-out and tedious process if you use the most optimized and effective available tools. Given here are some features of the tools that should be checked before you opt for one.

·        Handling large volumes of data – Make sure that the tool you choose is able to handle massive volumes of data effectively and without any performance degradation.

·        Completely automated – The tool should automatically merge, transform, and reconcile data through a simple point-and-click interface regardless of the quantum of data being replicated.

·        Be able to use SQL Server CDC – By using SQL CDC (Changed Data Capture) the tool should utilize database transaction logs at source and copy modified and changed data only to the Snowflake database. Complete data refreshes every time a change is made is thus not necessary.      

Most businesses today are data-driven and generate massive volumes of data. Not surprisingly, organizations are exploring new channels to enhance storage and computing facilities while ensuring that the data remains safe and secure.

One of the ways is to load data from Oracle to Snowflake.

Oracle has been meeting the data requirements of various enterprises since the commercial version was released in 1979. It ensures seamless data storage and retrieval and operates on a wide range of hardware and software including UNIX, GNU/LINUX, and Windows server. Its unique network enables most applications to be easily integrated into the Oracle database.

Given all these advantages where is the need to load data from Oracle to Snowflake? The answer lies in the many benefits that Snowflake has to offer.

Snowflake is a recently-introduced cloud-based data warehousing solution. Its multi-clustered shared data architecture allows users to quickly scale up or down in resource utilization. It has separate storage and computing facilities and users have to pay only for the quantum of data used.

Further, Snowflake allows multiple users to work simultaneously with multiple workloads and complex queries without any drop in performance or contention. These features are not available in the Oracle database, thereby making Snowflake the preferred option for data processing and storing.

There are other benefits offered by Snowflake.

In a world where data theft and breach are not uncommon in the business environment, Snowflake offers a high degree of data security and safety. All data is automatically encrypted with multi-factor authentication and federated authentication, thereby increasing safety manifold. Snowflake provides granular access control, thereby ensuring that all communication between databases and users is encrypted. Through third-party authentication and validation, stringent security standards like HIPAA are complied with.

Another feature that has been added recently and one which makes the option to load data from Oracle to Snowflake more attractive is database replication. It helps in non-business continuity and disaster data recovery. Secured data can be shared across regions in the cloud. The easy portability of data ensures quick loading of databases from Oracle or Microsoft SQL Server. All these are available in the new Standard version of Snowflake. In the older Enterprise for Sensitive Data (ESD) version, now known as Snowflake Business Critical (BC) edition, there is a Database Failover and Fallback feature for which Snowflake charges only if used.

This feature is especially useful in the case of an outage by providing users with seamless data recovery. When an outage occurs, secondary databases in the region in the cloud are triggered and act as primary databases until the outage is resolved. Once this happens users can set in motion the whole process in reverse to return to the primary databases to resume normal business operations.

Microsoft SQL Server is a relational database with several inherent advantages. The most important is that the complexity of installing the software is low as the setup wizard automatically detects and downloads all updates. This considerably reduces maintenance costs. Further, it is highly secured, using Policy-Based Management to detect non-compliant security policies.  

Why then would you want to replicate data SQL Server to Snowflake?

Snowflake is a cloud-based data warehousing solution that can hold both structured and unstructured data automatically. It manages how data is stored and organized and provides file size, structure, compression, metadata, and statistics. Storage is independent of computing and users can scale up and down in data usage, paying only for the quantum of resources used. It also enables multiple users to work simultaneously on multiple workloads without lag in performance.

What then should be the features of the tools that optimize the process to replicate data SQL Server to Snowflake?

Check for the following –

Fully automated – Select a tool that automates mergers, transforms, and reconciles data through a point and click interface. Regardless of the quantum of data to be replicated it speeds up the process. The traditional SQL Server data tools, on the other hand, require setting up of connectors and pipelines before replicating data to Snowflake. Additional coding too is often required.  

 Able to handle massive data volumes – The best of tools will be able to replicate huge volumes of Microsoft SQL data to Snowflake without any contention or drop in performance and speeds. Most tools are not up to this task, taking a long time to replicate data, thereby adversely affecting organizations’ productivity.

User-friendly – With a superior tool, there is no need for constant intervention by Database Administrators during replication. This leads to low Total Cost of Ownership (TCO) as DBAs do not have to spend hours to replicate data SQL Server to Snowflake. Managing dependencies and configuring backups are all automated in the best tools. The process becomes easier and less tedious.

Use CDC for replication – A tool where you have to opt for a full data refresh when you want to update changed source data at the destination is not ideal for you as it will affect your productivity and performance. Instead, the best is one that uses SQL Server CDC (Changed Data Capture) to Snowflake. The tool will utilize database transaction logs to query SQL Server data at the source. The modified and changed data will only be copied to the Snowflake database.  

Continuous data reconciliation – The chosen tool should be able to continually reconcile data in the Snowflake data warehouse. To replicate data SQL Server to Snowflake the option to validate data in the snowflake database continuously or at a frequency of choice should be available.

Oracle has many advantages – it can be ported to more than 100 hardware platforms and 20 networking protocols. Writing an Oracle application is quite safe from changes in direction in operating systems and hardware. Further, Oracle has the largest RDMS market share in VMS, UNIX, and OS/2 Server fields.

Given these multiple advantages, why would organizations want to migrate data Oracle to Snowflake?

Snowflake is a cloud-based data warehousing solution. It has addressed many issues that were hitherto inherent in traditional data warehouses leading to organizations preferring this platform. One of the many benefits is that it has separate computing and storage facilities and users can scale up and down in their utilization, paying only for the quantum of resources used. Also, there is no drop in performance and lag, even when multiple users simultaneously work with complex queries and multiple workloads.

Another advantage of Snowflake is that its architecture supports a wide range of cloud vendors. Users can, therefore, use the same tools to work with various cloud vendors without having to develop new skill sets.

Here is the process to migrate data Oracle to Snowflake.

Extracting data from Oracle

The first step is extracting data from Oracle to CSV file through a SQL Plus query tool in the Oracle Database Server. It can query and redirect the data to a CSV file with a “Spool” command which will continue writing till such time it is switched off. When incremental data has to be mined only, that is the changed records after the last pull, the tool has to be run in appropriate conditions.  

Converting and formatting the data

After the data is extracted from the Oracle database it has to be converted and formatted to match the requirements of specific organizations. It has to be ensured that the data character at the source matches those supported by Snowflake. This is not very complex as Snowflake supports almost all types of primitive and advanced data as well as nested data structures.

Uploading data to a cloud staging area

The next step to migrate data Oracle to Snowflake is to upload data to a cloud staging area before it can be loaded to Snowflake. Two staging areas can be utilized, internal staging and external staging. In the first instance, users and tables will be allotted to an internal stage and a name assigned to it. File format and date format will be applied. The whole process is automated. For external staging, Snowflake currently supports Microsoft Azure and Amazon S3 only.  

Copying data to table

Finally, to migrate data Oracle to Snowflake after staging, the “COPY INTO” command is used to transfer data to Snowflake. For this, the computing resources in Snowflake virtual warehouse are required and Snowflake credits are utilized.