Purpose-built for speed, agility, growth

Next-generation databases drive faster innovation

Evolution of data management

Over the past 80 years, data has become central to business planning and management, and has accelerated in importance in the 21st century. With data volumes soaring, databases and storage models have adapted accordingly.

1940s — 1970s

Databases are built on the hierarchical model. Data is stored on-premises.

Used For

Customer records
Transactions
Inventory management

1980s — 2000s

Relational databases, based on the SQL programming language, and network databases develop. Remote data centers emerge.

Used For

Analytics/business intelligence
Marketing campaigns
Enterprise resource planning (ERP)
Customer relationship management (CRM)

2000s

Web economy drives alternative NoSQL, or "not only SQL," databases. Data moves to the cloud, further fueling NoSQL.

Used For

Web and mobile apps
E-commerce
Big data
AI and machine learning

The next step:

Purpose-built, cloud-native databases

The latest generation of cloud-native databases perform specialized tasks much faster than the monolithic databases of the past.

SELECT DATABASE TYPE TO LEARN MORE

Document

Used For

Content management
Catalogs
User profiles

Graph

Used For

Knowledge graphs
Fraud detection
Social networks
Recommen-
dations

In-memory

Used For

Caching
Gaming leaderboards
Real-time analytics

Key-value

Used For

High-traffic web applications
E-commerce systems
Gaming applications

Ledgers

Used For

Systems of record
Supply chains
Registrations
Banking transactions

Relational

Used For

Enterprise applications
ERP
CRM
E-commerce
Analytics
Data warehousing

Time-series

Used For

Internet-of-things applications
DevOps
Industrial telemetry

Wide-column

Used For

Geographic information
Equipment maintenance
Fleet management

Document

A document database is designed to store “human-readable” data—that is, text-based data—in a format that’s standard for web and mobile applications.

Graph

Graph databases are for applications that need to navigate and query millions of relationships between highly connected graph data sets with millisecond latency at large scale.

In-memory

In-memory databases are used for applications that require real-time access to data. By storing data directly in memory, these databases deliver microsecond latency to applications for which millisecond latency is not enough.

Key-value

Key-value databases are optimized for common access patterns, typically to store and retrieve large volumes of data. These databases deliver quick response times, even in extreme volumes of concurrent requests.

Ledgers

Ledger databases provide a centralized and trusted authority to maintain a scalable, immutable, and cryptographically verifiable record of transactions for every application.

Relational

Relational databases store data with predefined schemas and relationships between them. These databases are designed to maintain referential integrity and strong data consistency.

Time-series

Time-series databases efficiently collect, synthesize, and derive insights from data that changes over time and with queries spanning time intervals.

Wide-column

Wide-column databases store vast amounts of data across multiple computers.

Who is using purpose-built databases?

Companies of all sizes and across all industries are benefiting today from the speed and flexibility a purpose-built approach delivers.

SELECT a company TO LEARN MORE

Stores and processes millions of data relationships in one system.

Audible for Business

The sound of simplicity

Leadership or communication? Team building or productivity? Emerging tech or workplace inclusion? Companies have a variety of personal and professional topics to choose from with Audible for Business, an audiobook offering designed to help corporations empower their teams through audio content. For that reason, Amazon-owned Audible, the world’s largest provider of audiobooks, chose a graph database to store the millions of data relationships it manages in Audible for Business, which involves thousands of titles.

Snapshot

“By using a fully managed graph database, our development team can manage the system without a dedicated DBA team.”

MAYANK GUPTA
Software Development Engineer

Clusters

2 million edges data relationships

2 million vertices data objects

Databases in Use

GRAPH

Audible for Business has also given company administrators for each account the ability to autonomously manage their users, determining, for example, which content is the most popular, or which users to add or remove, says Audible’s software development engineer Mayank Gupta.

Audible’s consumer business uses a number of purpose-built databases to store different types of information, including relational databases. But Audible for Business’s “one-to-many” and “many-to-one” relationships would have been unwieldy for a relational database—getting answers to a question like “Which benefits do we have for a specific customer?” would require intricate business logic and make it sluggish, Gupta says. Plus, using a relational database would require adding a special search engine such as Elasticsearch, which supports full-text search. That’s problematic since Audible stores personally identifiable information about users and can’t have data moving from one store to another. Using Neptune, Amazon Web Services’ graph database, allows Audible to remain safely inside its compliance requirements.

Simplicity of architecture gives the business operational efficiencies, too, says Gupta. “Managing a single system also makes it easier to manage resources and onboard new developers.”

Processes vast pools of data on a daily basis.

Janssen Pharmaceutical Companies of Johnson & Johnson

An advantage based in data

Janssen, the Pharmaceutical Companies of Johnson & Johnson, uses advanced analytics and data science to identify and address emerging challenges and opportunities for its customers and the market, giving the company a significant competitive advantage. Janssen compiles data about customers, prescriptions, drug use patterns, and more in a compliant way—traditionally storing and processing some 30 terabytes on relational databases. Previously, the company updated data weekly, as its applications required an entire week to process and analyze the data. That meant it had only one opportunity to slice and analyze the information before the arrival of a new week and a fresh set of data.

Snapshot

“The power of health care is data. So how can we make data more liquid, accessible, and actionable?”

DHARMESH THAKKAR
Senior Director, Business Technology

DATA VOLUMES

30 terabytes

Clusters

40-90 nodes

Databases in Use

RELATIONAL

In 2017, the company shifted its data environment to a combination of purpose-built databases offered by Amazon Web Services (AWS), enabling its pharmaceutical business to compliantly update and process data daily, rather than weekly. Janssen also used AWS’s databases to build a data lake that analysts can quickly and easily query themselves in a self-service environment, helping “reduce the time to insights,” says Dharmesh Thakkar, Janssen’s senior director of business technology. The cloud-based database and analytics stack also enabled a shift to predictive insights, transforming the way Janssen delivers products and services to its customers.

Gathering insight quickly—as well as tapping into new types of insights—gives Janssen an edge in a dynamic, tech-enabled health-care marketplace.

Innovates more quickly and focuses on its core competency.

Lexia Learning

Getting a read on data

Lexia Learning, a Rosetta Stone company, is one of the world’s best-known and most highly regarded reading-technology companies. Its real-time interactive learning software is used by millions of students around the world. Despite being a midsize company, Lexia has significant data needs as it engages with these students, who expect lightning-fast performance.

Snapshot

“When you grow up on physical hardware, you build product accordingly. In the cloud, applications are built for the cloud.”

ROB PAUSHTER
Vice President, Product Development

DATA VOLUMES

5 terabytes

Databases in Use

RELATIONAL

IN-MEMORY

With data stability and speed critical to its business, Lexia was one of the early adopters of Amazon Aurora, a cloud-native relational database from Amazon Web Services (AWS). In 2017, the company piloted an initiative that began a two-year transition from open source MySQL databases housed in data centers to Aurora. Lexia handles searching with the NoSQL ElastiCache.

Using Aurora and the AWS platform, Lexia has improved flexibility, capacity, and redundancy. The company auto-scales storage and processing capacity as needed and has multiple copies of master data. And it no longer needs to worry about hardware infrastructure issues.

The additional flexibility and speed gained in the process also allow Lexia to innovate and try new ideas more quickly. Free of hardware infrastructure concerns, the company now focuses on its core competency—teaching the world to read.

Audible for Business

The sound of simplicity

Leadership or communication? Team building or productivity? Emerging tech or workplace inclusion? Companies have a variety of personal and professional topics to choose from with Audible for Business, an audiobook offering designed to help corporations empower their teams through audio content. For that reason, Amazon-owned Audible, the world’s largest provider of audiobooks, chose a graph database to store the millions of data relationships it manages in Audible for Business, which involves thousands of titles.

Snapshot

“By using a fully managed graph database, our development team can manage the system without a dedicated DBA team.”

MAYANK GUPTA
Software Development Engineer

Clusters

2 million edges data relationships

2 million vertices data objects

Databases in Use

GRAPH

Audible for Business has also given company administrators for each account the ability to autonomously manage their users, determining, for example, which content is the most popular, or which users to add or remove, says Audible’s software development engineer Mayank Gupta.

Audible’s consumer business uses a number of purpose-built databases to store different types of information, including relational databases. But Audible for Business’s “one-to-many” and “many-to-one” relationships would have been unwieldy for a relational database—getting answers to a question like “Which benefits do we have for a specific customer?” would require intricate business logic and make it sluggish, Gupta says. Plus, using a relational database would require adding a special search engine such as Elasticsearch, which supports full-text search. That’s problematic since Audible stores personally identifiable information about users and can’t have data moving from one store to another. Using Neptune, Amazon Web Services’ graph database, allows Audible to remain safely inside its compliance requirements.

Simplicity of architecture gives the business operational efficiencies, too, says Gupta. “Managing a single system also makes it easier to manage resources and onboard new developers.”

Janssen Pharmaceutical Companies of Johnson & Johnson

An advantage based in data

Janssen, the Pharmaceutical Companies of Johnson & Johnson, uses advanced analytics and data science to identify and address emerging challenges and opportunities for its customers and the market, giving the company a significant competitive advantage. Janssen compiles data about customers, prescriptions, drug use patterns, and more in a compliant way—traditionally storing and processing some 30 terabytes on relational databases. Previously, the company updated data weekly, as its applications required an entire week to process and analyze the data. That meant it had only one opportunity to slice and analyze the information before the arrival of a new week and a fresh set of data.

Snapshot

“The power of health care is data. So how can we make data more liquid, accessible, and actionable?”

DHARMESH THAKKAR
Senior Director, Business Technology

DATA VOLUMES

30 terabytes

Clusters

40-90 nodes

Databases in Use

RELATIONAL

In 2017, the company shifted its data environment to a combination of purpose-built databases offered by Amazon Web Services (AWS), enabling its pharmaceutical business to compliantly update and process data daily, rather than weekly. Janssen also used AWS’s databases to build a data lake that analysts can quickly and easily query themselves in a self-service environment, helping “reduce the time to insights,” says Dharmesh Thakkar, Janssen’s senior director of business technology. The cloud-based database and analytics stack also enabled a shift to predictive insights, transforming the way Janssen delivers products and services to its customers.

Gathering insight quickly—as well as tapping into new types of insights—gives Janssen an edge in a dynamic, tech-enabled health-care marketplace.

Lexia Learning

Getting a read on data

Lexia Learning, a Rosetta Stone company, is one of the world’s best-known and most highly regarded reading-technology companies. Its real-time interactive learning software is used by millions of students around the world. Despite being a midsize company, Lexia has significant data needs as it engages with these students, who expect lightning-fast performance.

Snapshot

“When you grow up on physical hardware, you build product accordingly. In the cloud, applications are built for the cloud.”

ROB PAUSHTER
Vice President, Product Development

DATA VOLUMES

5 terabytes

Databases in Use

RELATIONAL

IN-MEMORY

With data stability and speed critical to its business, Lexia was one of the early adopters of Amazon Aurora, a cloud-native relational database from Amazon Web Services (AWS). In 2017, the company piloted an initiative that began a two-year transition from open source MySQL databases housed in data centers to Aurora. Lexia handles searching with the NoSQL ElastiCache.

Using Aurora and the AWS platform, Lexia has improved flexibility, capacity, and redundancy. The company auto-scales storage and processing capacity as needed and has multiple copies of master data. And it no longer needs to worry about hardware infrastructure issues.

The additional flexibility and speed gained in the process also allow Lexia to innovate and try new ideas more quickly. Free of hardware infrastructure concerns, the company now focuses on its core competency—teaching the world to read.