Does every organization put customer experience and needs at its forefront? The truth is a big NO. And when it comes to Oracle, it’s a strict no from the customers.
Oracle is still in the database race only because of the lock-in it has got over its unfortunate customers as hostages. But the reality is, Oracle is on the verge of its extinction in the marketplace and the customer growth rate is diminishing.
As an Oracle customer or developer, you may be shocked to know that Oracle is losing the database race. Only because Oracle is excruciatingly difficult software to migrate, many organizations are sticking to it. Migrating the entire data architecture that has been built over the years is not a cakewalk for any organization and the decision-makers get flinched to hear the need for data platform modernization. The major reason for the fall of Oracle is its “One size fits all” attitude and failure to innovate or reengineer its products as per market needs.
If you’re an Oracle lover, you may defend the aspects of innovation with the new Oracle cloud products like autonomous data warehouse. But the real-time challenge in adopting these Oracle cloud products starts from purchasing a separate license for Oracle cloud hosting platform that will burn a huge hole in the pockets of enterprises.
By now many enterprises have already embraced AWS/Azure/Google cloud platforms to host their business-critical applications. Adding up Oracle cloud only to manage their data layer will only double up their infrastructure costs & maintenance efforts. Another major fallback of Oracle cloud data warehouse is that it requires a full process cycle to scale up or scale out resources even for any critical remedial activities. If users are in need of additional resources, they need to pay upcharges on their Oracle licensing terms and then perform their operations. Once, the database operations are completed, they must reach out to Oracle team and scale out the resources. And it’s tiring to always reach out to support or sales team, to renew the licensing terms for scaling up or scaling out resources based on your operational needs.
As of now, these hints would have become an eye-opener for you to realize the need for data platform modernization. If you’re still wondering,
“if not Oracle, then what?” – It’s Snowflake cloud data platform
In this article, let’s discuss the top 10 reasons for the fall of Oracle and how Snowflake is winning the competitive marketplace in these aspects.
Cost-Efficiency
Oracle always has a dominant attitude of “One-size fits all businesses & operations” in every aspect and the licensing terms are one among them. Now, Oracle pushes its customers to their cloud products through tricky sales strategies. If the sales strategy fails, then Oracle slams customers with audit fines for non-compliance in licensing terms.
Isn’t it the utmost trap a customer could face?
At the same time, while picking the Oracle cloud products, the licensing cost doubles up with the cost of Oracle cloud. All the Oracle products have fixed subscription plans and licensing terms. For every user and CPU processor, you opt-in, the cost increases in Oracle. In addition, there will be upcharges for other hardware and software counterparts.
Even though Oracle boasts about its new products like autonomous cloud data warehouse & 18 C in the aspects of features, lower operational cost, and automated database maintenance, they have not reduced their licensing fee even a bit. Despite the lags in features & functionalities, and rigidness to support other cloud hosting platforms or scalability of resources, Oracle has always fixed its licensing terms at a rocketing price than competitors.
On the contrary, the emerging data platform winner Snowflake has a flexible pay-as-you-go pricing model. The licensing fee is split as on-demand storage and compute charges. Snowflake bills storage as terabytes per month and compute resources on a per-second basis.
On top of this, if the customers have planned to pre-purchase capacity storage, Snowflake offers it at half the price. With this flexible pricing model, businesses can save huge costs that are spent on data storage. Be it small, medium, or large enterprise, their Snowflake storage cost wouldn’t exceed $50-100. Further, Snowflake provisions the utmost elasticity to scale up and scale down compute resources, thereby tuning the licensing costs as per customer needs.
Snowflake is truly a bliss to modern enterprises in the aspects of cost savings. Yet another exciting pocket-saving feature of Snowflake is its data transfer pricing model. Snowflake users can transfer data within the same cloud platform and the same region at zero cost.
Installation
Oracle requires a whole bunch of hardware, software including operating systems, and highly skilled database administrators for installation. Further, Oracle database administrators must plan for partitioning, backup and recovery, remedial patchworks, feature upgrades, and other activities with downtime periodically. Betting on the fact that, at least on monthly basis, enterprises leveraging Oracle must tune in their database with planned downtime of 10-15 hours.
In Snowflake data platform, there is no need for hardware, software, or operating system requirements for installation. You don’t need expert database administrators to plan or tune storage capacity, partitioning, compress the data, backup & recovery, or any remedial patchworks. Snowflake has advanced database remedial capabilities with columnar data compression, time travel, and automated scale-up and scale-out options. In fact, the last ever database activity your organization is performing will bemigrating from Oracle to Snowflake.
Data Engineering
Right from source touchpoints data collection to warehousing, Oracle would encompass “n” number of PL/SQL stored procedures & functions with thousand lines of code. Oracle supports only native PL/SQL stored procedures and functions for its data engineering operations. Oracle lags in the compatibility of other coding languages and platforms. Further, highly skilled data engineers are required to perform data collection, ingestion, cleansing, refactoring, and enriching the data as meaningful business insights in Oracle data warehouse.
Snowflake has partnered with leading platforms for seamless data engineering operations. Some of the compatible data engineering platforms with Snowflake native connectors are Informatica, Talend, Matillion, Fivetran, dbt, Spark, Java, and Python. For instance, the native Snowflake connector for python provisions enterprises to build applications on python with Snowflake as a transactional data layer. In addition, Snowflake has many native product components like Snowpipe, Snowpark, Snowstream, Snowtasks, and much more for data engineering operations. Depending on your existing data architecture, you can jumpstart to build a matured data model in Snowflake data platform with desired tool stack.
Snowflake supports ELT approach by offering native connectivity to a wide variety of data integration tools such as Matillion, dbt labs, Fivetran, Azure Data factory , Informatica, Talend , boomi , Kafka, Qlik and Rivery. With these native connectors for data integration, enterprises can easily connect applications and data from multiple sources and unify data warehousing.
Snowflake is always ahead on the race of customer satisfaction and innovation.
Yes, Snowflake has introduced a new workload “Unistore” for its customers to enable hassle-free data operations. With Unistore’s modern approach, Snowflake is facilitating enterprises to maintain a unified data layer that brings in transactional business applications and analytical data in near real-time. If Snowflake says it as managing data in a unified platform, they truly mean it.
Yes, The enterprises can standardize their security and governance controls in the Unistore by eliminating the need to manage data silos or copy clones.
Data Compression
Oracle users must plan and optimize every database query based on the primary or foreign keys, and indexes to avoid hanging queries. If there are any long-running queries in Oracle, DBAs must abruptly abort the queries and functions manually to recover the availability of database. Oracle DBAs must plan for data compression periodically to maximize the storage capacity and reliability of database.
If you’re a Snowflake user, you can completely sit back and relax in the aspects of data compression. Data engineers need not carefully optimize data insert & append queries, rather Snowflake performs automated columnar compression and stores datasets in a columnar format in databases. The data is compressed around 3-6X times more than raw data in Snowflake platform. With the modern approach of NoSQL query search pattern, micro-partitioning of large tables and data lake storage in Snowflake, data transactions take place within minutes or even seconds by leveraging this columnar database structure and compressed datasets. Beyond, these automated data compression features, Snowflake platform handles the long-running queries with automated alerts and cancellations based on the customized rules to avoid hangover of databases.
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Data Sharing
Oracle users can share only configuration data such as jobs and payment terms with other users inside the organization. Oracle does not permit data sharing with third parties. In the data sharing feature, Oracle has again lost the race.
Snowflake users can seamlessly share enterprise datasets with customers, third parties, and partners by leveraging Snowflake Data Exchange platform. Snowflake users can share their data with third parties by signing up for a reader account on the platform. In terms of legacy data warehouses, you need to download/export data and then share it with specific tools for the accessibility of business partners and third parties. Whereas, with the Snowflake data cloud, you can share data with existing snowflake users or create a reader account for non-snowflake users and enable zero-copy clone accessibility. The changes made in source data of Snowflake platform are automatically reflected in the third-party or customer clone. The data sharing features on the Snowflake are with no extra infrastructure cost. This architecture of Snowflake can cut down the cost spent on data-sharing tools and other resources.
Workloads
Oracle supports transactional & analytical workloads. Whereas Snowflake has a matured cloud data platform that supports data lake, data applications, data engineering, data science, data warehouse, and data sharing under one roof.
Snowflake supports a wide range of workloads such as data applications, data engineering, data science, data warehouse, data lake, and data sharing. Snowflake offers various native tools for data engineering operations to establish a cohesive data model and a single source of truth. Successively, Snowflake enables enterprises to democratize business intelligence at all levels and drive businesses with data-driven decisions on its platform.
Snowflake has brought a revolutionary architecture of workload isolation. Users can deploy an unlimited number of virtual warehouses for various workloads in Snowflake platform. For instance, a user can ingest terra or petabytes of data in one virtual warehouse, while simultaneously performing data engineering operations such as cleansing, optimizing, or refactoring datasets on another and customizing the business intelligence reports on another virtual warehouse. Snowflake provides utmost flexibility on independent workloads and warehouses for its users. Overall, Snowflake is the holistic data platform for all your enterprise data engineering needs with flexible and reliable architecture.
Deployment
Oracle database can be deployed only in on-premises or Oracle cloud environments. By now, you’d have recognized the high cost that’s added to your data layer if you’re using Oracle products.
Remember, “Oracle cloud data warehouse is not cloud-agnostic”
So, you need to employ an additional workforce to maintain the Oracle cloud platform up and running.
Snowflake runs on all the leading cloud platforms such as AWS, Azure, or Google Cloud. As mentioned, Snowflake has flexible pay-as-you-go pricing for both storage and compute resources. Storage costs are incurred by cloud providers as terra bytes per month and there are zero hidden charges from Snowflake for storage or deploying on cloud hosting platforms.
Database Maintenance
If you’re an Oracle user, by this time you’d have probably felt that you’re still in the stone age in the aspects of database maintenance.
Right from storage capacity planning, remedial patchworks, partitioning, indexing, feature upgrades, performance tuning, backup & recovery, to other database activities should be planned and executed by highly skilled database administrators. Oracle releases feature upgrades and patches at regular intervals, thereby DBAs must plan for downtime to adopt the new features of the software. Also, DBAs must be highly skilled with hands-on expertise to surpass through system failures while partitioning and indexing the database initially.Even though Oracle boasts about automated database maintenance in its recent products such as 18C, and autonomous data warehouse, it’s not quite convincing. Oracle users can gather the workload statistics on database for a specific period and plan to optimize database activities further. However, the workload statistics gathering is not recommended for the large volume of datasets in Oracle. You’d probably get trapped in a database hungover if you perform large data statics gathering. Oracle always requires a human administrator to tune its performance and reliability.
On the contrary, Snowflake provides a completely automated database platform. Snowflake bets on near-zero database administration efforts with automated remedial activities, columnar compression, database statistics, query & performance tuning, scalability, 90 days of time travel, no partitioning/ indexing, and what not.
Snowflake simply works independently in every aspect of database administration.
Snowflake users are highly satisfied with the complete automated maintenance of the platform than Oracle or any other legacy database.
Scalability
Everybody in the industry accepts the rigidness of Oracle in terms of price as well as scalability. Be it storage or compute clusters, Oracle has rigid architecture. Enterprises must upgrade the hardware or convert to Real Application Clusters (RAC) to scale up even for an hour. Similarly, to scale out there is no option in Oracle. The computing power and storage utilization hit the roof when concurrent queries are performed. Above all, Oracle users must purchase compute clusters and storage and pay for even the unused resources every time they renew licensing terms.
Inversely, Snowflake provides complete elasticity in architecture, pricing, and other aspects. Snowflake can expand from 1 to 128 compute clusters and 1.3 TB data of query within milliseconds. In Snowflake, scalability is just a matter of T-Shirt size from XS to XXXXL. Users can choose to auto suspend or manually suspend the unused compute resources in Snowflake and pay for only what is consumed.
Disaster recovery & reliability
Oracle has Real Application Clusters (RAC) to enable users to run a single database across multiple servers to maximize reliability and scalability. Users must plan for additional standby servers for disaster recovery and reliability of the on-prem Oracle database. Yes, Oracle does offer a “Data guard” feature for automated recovery of failover databases but at the cost of additional licensing fees.
On contrary, Snowflake automatically performs geological data replication to three cross-cloud data centers at cross regions to enhance the reliability of data platform. There’s no need for a standby server, data center, or any other additional counterparts for high availability and disaster recovery in Snowflake data platform.
Wrapping up
On the whole, while existing Oracle customers were looking for extraordinary cloud products, Oracle released a cloud data warehouse that doesn’t fit for modern enterprise needs anymore. Whereas Snowflake emerged in the data platform race a decade ago and conquered the large pool of happy customers with its flexible pricing model and architecture. Also, an alarming fact is if you’re not modernizing your legacy Oracle data landscape at this point of time, you will end up piling a huge data pool and make it complex to modernize your enterprise data landscape forever.
If you’ve decided to shift to matured data models with a wide variety of workloads, then we would highly recommend trying out Snowflake. Hurry up, We can help you through the data platform modernization journey from assessment to cutover of legacy systems.
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