Essential reading: Explaining modern data management (Part 3)

In previous posts, we covered the concepts and four architectures to build a modern data platform. Now, we’re wrapping up this series by looking at three companies that successfully implemented Oracle solutions. Each company is unique—different technologies and even different clouds—but all of them reached their goals.

Lyft on Autonomous Database

Lyft, the transportation network, is busy reimagining the future of transportation. Behind the scenes, though, the company had gone from a high-growth startup to a publicly traded enterprise processing billions of transactions a year, and its finance systems hadn’t kept up. It was running about 30 systems that were siloed, costly to maintain, and inefficient. They didn’t provide the timely information that a fast-growing business needs to make decisions. Lyft chose Oracle Database Platform that we presented, with Oracle Autonomous Database as the data platform. They benefited from its built-in integration with their Oracle Fusion Cloud Enterprise Resource Planning (ERP) and Oracle Analytics Cloud.

Following the implementation, Lyft closes their financial books faster and allows more stakeholders access to centralized data, including meaningful visualizations that lead to better business decisions.

Bionime on MySQL Heatwave on AWS

Bionime’s vision is to provide diabetes patients with peace of mind through self-monitoring blood glucose systems that help them accurately manage and control their health.

The medical device manufacturer’s analytics service was built around Amazon Web Serviced (AWS) Relational Database Service (RDS) and wasn’t performing for the large amount of data needed to provide accurate and up-to-date glucose monitoring for patients. Bionime sought a faster data platform that would support its existing technologies.

Bionime selected Oracle MySQL HeatWave on AWS for a fully managed database service that combines transaction processing with an in-memory query accelerator for a high-performance analytics engine. MySQL HeatWave eliminated the need for a separate analytics database and extract, transform, load (ETL) processes. Real-time analytics helps Bionime accelerate the development of its glucose monitoring systems but with less database administration. Bionime has seen improvements of 50-times faster resolution of complex queries compared to AWS RDS.

Experian using managed open source services

With almost 18,000 employees in over 40 countries, Experian manages the credit history data on 1.3 billion individuals and 166 million businesses across the globe. They needed an open data platform with open source products and services to build vast data pipelines and process large amounts of data, including real-time events. The company wanted to unify critical data and analytics from on-premises data centers, co-location facilities, and cloud providers onto a single lakehouse architecture. Experian also had a significant investment in relational databases that needed to be integrated. To accomplish all this, Experian chose the managed open source for big data platform.

They migrated open source workloads on Spark and Hadoop from other cloud providers to Oacle Cloud Infrastructure (OCI) Big Data and OCI Data Flow services without requiring reengineering or rearchitecting, which contributed to significant cost and time savings. Experian’s success in the migration to OCI Lakehouse was also supported by Oracle Cloud Lift Services, which provided guidance on planning, architecting, and prototyping to help deliver immediate value.

With this architecture in place, Experian saw a 40% increase in performance, a 60% reduction in costs, and increased reliability and resilience.

Conclusion

We started this series by observing that data is key to business success; Lyft, Bionime, and Experian are only a few examples. They apply Oracle’s commitment to customers to meet you where you are in your journey to the cloud, ensure portability across deployment options, and avoid vendor lock-in.

We believe that the Oracle Data Platform is uniquely positioned to enable you to achieve success with your data with the following capabilities:

◉ Combine transactional and analytical data and avoid siloes

◉ Use OCI, Oracle software as a service (SaaS), or any amount in between. You select the amount of control

◉ Bring any kind of data to the platform, and we provide tools to help you do it. You can also use your own!

◉ Explore the power of OCI and other clouds. We meet you where you are.

◉ Use leading Oracle Analytics Cloud reporting or any third-party analytical application. OCI is open!

Source: oracle.com

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Essential reading: Explaining modern data management (Part 2)

In our last post, we reviewed the evolution of data warehouses, big data, and data lakes. We also mentioned that today’s needs in data management include the following examples:

◉ A unified data platform

◉ Support for open source

◉ Integration of artificial intelligence (AI) and machine learning (ML)

◉ Pay-as-you-go pricing

◉ Support for multicloud

Other cloud platform vendors have responded by releasing multiple services that are tailored to specific use cases. This choice often leads to an expansion of services rather than a holistic solution. For example, Google Cloud offers eight different data management products (with significant overlap). Amazon Web Service (AWS) offers at least ten.

The challenge is that you must figure out which service—or combination of services—is best for your needs. Each extra service adds data movement, design, and maintenance. That’s not really a unified platform but a collection of parts with the drawback of more administration.

In contrast, Oracle has chosen to take its popular, proven database experience and break down traditional barriers encountered by other databases. The Oracle Cloud Infrastructure (OCI) Data Platform has the following features and capabilities:

◉ Combines transactional database, data warehouse, and data lake capabilities into a single platform of choice

◉ Supports all types of data (structured, semi-structured, and unstructured)

◉ Supports all usage scenarios (reporting, analytics, artificial intelligence, and business applications) with predefined integrations with software as a service (SaaS)

◉ Runs on major cloud platform vendors, with OCI offering the most options

Let’s review the architectural choices that Oracle offers so that you can select the best data platform for your needs.

Oracle Autonomous Database platform

If most your data is already inside a relational database in a structured format and you’re planning expansions, then the all-in-one Autonomous Database platform is the solution. Oracle Autonomous Database is the well-known Oracle database that adds zero administration and includes spatial, graph, JSON, MongoDB API compatibility, machine learning, and artificial intelligence capabilities. Instead of choosing between transactional and analytical capabilities, you can get both in one system.

Autonomous Database also extends its reach to OCI Object Storage, AWS S3, or any S3-compliant object storage. You can read and query object data just like tables, using the full power of the Oracle Database. This capability is especially exciting for data engineers and data scientists, who can examine object data with familiar Oracle SQL and then combine those results with existing data in the Oracle database for rich results. Autonomous Database supports data in object storage that are in delimited text, JSON, Parquet, Avro, and ORC formats.

MySQL Heatwave

Maybe you’re more familiar with the world’s most popular open-source database: MySQL. Oracle now offers MySQL HeatWave, which is a managed database service that combines transactions, analytics, and machine learning into a single cloud offering. It delivers real-time, secure analytics without the complexity, latency, and cost of extract, transform, and load (ETL). MySQL HeatWave is available on OCI, AWS, and Microsoft Azure.

MySQL HeatWave adds a scalable, analytical engine that transparently and continuously mirrors the data in MySQL. As each query runs, HeatWave automatically evaluates the query and responds from the analytical engine when advantageous. No change is needed to your applications.

HeatWave also accelerates machine learning by letting you build models in the service instead of having to export data out to yet another specialized service. Developers and data scientists can continue using their existing tools, such as SQL, Jupyter, and Apache Zeppelin, while working with the data in a single environment.

Like Oracle Autonomous Database, Oracle has enhanced MySQL to directly read data in object storage. We call that feature the MySQL HeatWave lakehouse. The lakehouse gives developers and data scientists a unified platform to run transactions, analytics, and machine learning applications.

Managed open source for Big Data

The previous scenarios are a great fit for data that is mostly structured as well as for developers with experience in the leading database technologies. But what if your data is mostly unstructured? What if you’re more experienced with Hadoop-based environments?

Oracle offers several managed services based on Spark and Hadoop to ease your migration into the cloud while using your familiar tools. Using services based on open-source technologies affords you more control over your operations while running on the secure and reliable OCI infrastructure. With this architecture, you can quickly move your existing cluster into OCI (or create a new data lake).

You can complement these services with easy-to-use artificial intelligence services, such as speech-to-text, sentiment analysis, image analysis, and more.

Modular approach

Finally, some advanced scenarios don’t match any of the previous options. You need specific capabilities, and you need to assemble them in a particular manner. Oracle’s comprehensive portfolio of services allows you to build your own, personal data platform.

Source: oracle.com

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