In today's digital landscape, data is the lifeblood that fuels growth, innovation, and your bottom line. Your business needs accurate data-driven insights to make decisions, manage operations, and create value. That means the need for efficient and effective data management has never been more crucial. And you can’t have those when you’re wasting time and money managing physical data infrastructure.
Companies have been migrating to cloud data services for about a decade. These fully managed data capabilities take the burden of data management off your shoulders. Instead of managing your own messy data infrastructure, you can focus resources on core business goals, speed up software development, and build modern applications at scale.
Here’s what you need to know to get started with fully managed data in the cloud.
What are fully-managed data capabilities?
Fully-managed data capabilities are cloud-native services that handle the infrastructure and operational aspects of managing your data operations. Managed data services come with scalability, performance optimization, security and compliance, data integration, backup and recovery, automation, and expert support.
Instead of juggling racks of complicated hardware and data management simultaneously, you can just focus on getting DataOps and analytics right. Fully-managed data is everything you need – from cloud data warehouses to data science pipelines – all in the cloud.
This approach to data management accelerates the software development process, reduces costs, and improves the reliability, performance, and security of all your applications. And fully managed data services have matured so you can quickly scale your business without a high risk of downtime, data breaches, and getting locked into monolithic data solutions.
Here are a few examples of global products that fully manage their data in the cloud.
What products are made possible by fully managed data services?
The biggest platforms and products in the world trust their large-scale data requirements to the cloud. That’s because it comes with high availability, scalability, and performance. It’s often better and more cost-effective than you could do on your own – even after years of investment.
Most of the globe's successful brands use some mix of cloud data services.
- Amazon – As the world's largest e-commerce company, Amazon provides fully-managed data services through Amazon Web Services (AWS) and relies on these services internally. For instance, Amazon uses its own DynamoDB, a fast and flexible NoSQL database service for any scale, to handle billions of requests per day.
- Netflix – Netflix uses Amazon Web Services (AWS) for much of its data infrastructure. AWS provides a range of fully-managed services that Netflix uses, including databases, machine learning tools, and data warehousing services. These services allow Netflix to handle massive amounts of data and deliver a seamless streaming experience to its millions of users worldwide.
- Twitter – Twitter uses Google Cloud Platform, with a highly-scalable and fully-managed NoSQL database service to handle and store its massive influx of tweets and user data daily. Short of what Musk has done to the company, it’s still an incredibly complex web of technology that serves millions of users (mostly successfully).
- Shopify – Shopify hosts hundreds of thousands of online stores and uses Google Cloud's fully-managed database services to ensure they can scale during peak shopping periods, like Black Friday and Cyber Monday. Google's Cloud Spanner, a globally distributed and strongly consistent database service, is an example of a managed service they use.
- Alibaba – Alibaba, one of the largest e-commerce platforms globally, has its own cloud computing division, Alibaba Cloud, providing various fully-managed services. Alibaba uses these services, such as ApsaraDB for RDS, a relational database service, to manage its extensive customer and transaction data.
- DoorDash — DoorDash uses fully managed data services to store and process data related to orders, customer preferences, and restaurant listings. They rely on services like Amazon RDS and Amazon DynamoDB to handle their data requirements, ensuring that their platform can efficiently manage the high volume of transactions and provide a reliable service to their users.
- Etsy – Etsy uses Google Cloud’s fully-managed services like Bigtable, a wide-column database service, for handling the billions of reads and writes to their database daily.
- Airbnb – Airbnb uses Amazon Redshift, a fully-managed data warehouse that makes it simple to analyze all their data using standard SQL and their existing business intelligence tools, to make informed decisions quickly.
- Lyft – Lyft supports its high-volume ride-tracking system, using Amazon DynamoDB, a key-value and document database that delivers single-digit millisecond performance at any scale.
The list goes on and on. If you have a digital product, platform, or tech stack building fully managed data capabilities are often the best choice.
What are some use cases for fully managed data?
From real-time analytics and IoT applications to AI/ML and marketing personalization – fully managed data makes it all possible. Most of the challenge in transforming your business comes up when you get to data and infrastructure (especially if it’s legacy).
When you migrate your data management to the cloud, you can scale, innovate, and build products faster while saving money.
- Multiplayer gaming and commerce – Multiplayer online games need to handle real-time interactions between millions of players worldwide. Fully-managed data services provide the low-latency data processing and global scalability required for these games. They also make it possible to add secure commerce features.
- Real-time analytics – For products that require real-time insights, such as recommendation systems or fraud detection systems, fully-managed data services deliver the necessary speed and scalability. They handle large volumes of data in real-time and enable instantaneous data processing and analytics.
- IoT applications – Internet of Things (IoT) applications often generate vast amounts of data that must be processed and analyzed in real-time. Fully-managed data services give you the scalability and real-time processing capabilities required for these applications.
- Machine learning and AI – Developing AI models and machine learning techniques require access to large amounts of data. Fully-managed data services store and manage this data. Some cloud data providers like AWS even provide integrated machine learning services, making it easier to develop, train, and deploy AI models.
- Mobile applications – For mobile applications that need to handle user data, fully-managed data services provide a scalable and reliable backend. They handle tasks like user authentication, data storage, and real-time updates. This frees up your developers to focus more on the front-end user experience.
- Ecommerce platforms – Ecommerce platforms must handle huge volumes of transactional data, user data, and product data. Fully-managed data services can provide the necessary data management capabilities, including transaction processing, data analytics, and data security.
- SaaS applications – Software as a Service (SaaS) applications require multi-tenant data management – where data from many customers must be kept isolated and secure. Fully-managed data services handle this complexity while providing scalability and reliability.
- Efficient supply chain management – Gain real-time visibility into your supply chains to optimize inventory management, streamline logistics, and reduce operational costs.
- Personalized omnichannel marketing – Fully-managed data services can store and process large amounts of customer data, enabling real-time personalization based on customer behaviors and preferences.
While all these capabilities come from the cloud, they’re made up of a complex tech stack you need to know a little bit about.
Technologies and tools that make fully managed data possible
From infrastructure to extract, transform, load (ETL) tools – these are the fully managed data tools you need to know about. Technically, your DataOps team needs to know them, but it’ll help if you know the big ones.
- Cloud infrastructure platforms – These replace your racks of internal servers and provide the infrastructure for storing and processing data.
- Amazon Web Services (AWS) – Offers services like Amazon RDS (relational database service), Amazon DynamoDB (a NoSQL database service), and Amazon Redshift (a data warehousing service).
- Google Cloud Platform (GCP) – Provides services like Google Cloud SQL, Google Cloud Spanner (a relational database service), and BigQuery (a data warehousing service).
- Microsoft Azure – Includes services like Azure SQL Database, Azure Cosmos DB (a globally distributed, multi-model database), and Azure Synapse Analytics (a data warehousing service).
- Managed databases – There are various types of databases for different needs:
- Relational databases – MySQL, PostgreSQL, Oracle Database
- NoSQL databases – MongoDB, Apache Cassandra, Couchbase
- Time-series databases – InfluxDB, TimescaleDB
- Graph databases – Neo4j, Amazon Neptune
- Data warehousing solutions – These tools help with storing, querying, and analyzing large datasets.
- Google BigQuery
- Amazon Redshift
- Microsoft Azure Synapse Analytics
- ETL (Extract, Transform, Load) tools – These are used to move and transform data.
- Apache NiFi
- Informatica PowerCenter
- Data integration tools – These help to integrate data from different sources.
- Dell Boomi
- Apache Kafka
- Data security tools – These tools provide encryption, network security, and access control.
- Symantec Data Loss Prevention
- Check Point Data Security
- McAfee Total Protection for Data Loss Prevention
- AI and machine learning platforms – These platforms can be used to enhance data management.
- Google Cloud AI and Machine Learning
- AWS SageMaker
- Containerization and orchestration tools – These tools help with the deployment and management of microservices and applications.
- Docker – For creating and managing containers
- Kubernetes – For orchestrating and managing containerized applications
- Data governance tools – These tools ensure that data is used and managed in compliance with regulations and policies.
- IBM Data Governance
- Monitoring and alerting tools – These tools help monitor the health and performance of the data services.
- Prometheus – An open-source systems monitoring and alerting toolkit
- Grafana – An open-source platform for monitoring and observability
- AWS CloudWatch – A monitoring and observability service built for DevOps engineers, developers, site reliability engineers (SREs), and IT managers.
Remember that implementing these technologies still requires a DataOps team with a wide range of skills and expertise. With the right team in place, these are the high-level benefits that come with fully managed data capabilities.
CIOs look for these benefits of fully managed data
Sure, you’ll save money on infrastructure with fully managed data. But you also get access to cutting-edge technology that can grow your business. In the long run it’s hard to isolate the whole picture of ROI you can get from fully managed data because there are so many possible benefits. And if you get just half of this list from migrating your data capabilities to the cloud, the sky's the limit for company revenue.
- Cost efficiency – Fully managed data services follow a pay-as-you-go pricing model that saves you money compared to paying for in-house data infrastructure. These services eliminate the need for upfront hardware investments and reduce ongoing maintenance, personnel, and resource management costs.
- Focus on core business – By offloading the responsibilities of data infrastructure management to the cloud provider, CIOs and VPs of Engineering can dedicate more time and resources to core business objectives and innovation.
- Scalability and performance – Fully managed data services can automatically scale with the organization's needs and ensure optimal performance even during periods of high demand. This allows your business to grow and adapt more efficiently without worrying about the limitations of physical data infrastructure.
- Improved reliability and availability – Managed data services typically offer built-in redundancy, backup, and disaster recovery features that help ensure high data availability and durability. This minimizes the risk of data loss and downtime to protect your operations and reputation.
- Enhanced security and compliance – Cloud providers invest heavily in securing their data infrastructure – employing advanced encryption, access control, and network security measures. They also handle compliance with various data protection regulations. That reduces the burden on you to navigate complex legal requirements.
- Enhanced security and privacy – Fully managed data services offer robust security features and help you comply with data protection regulations. This allows your business to build trust with customers by safeguarding their data and ensuring the privacy of their personal information. And it makes it easier to spread across verticals, countries, and markets with different data security requirements.
- Access to cutting-edge technology – Cloud providers continually update and improve their managed data services. That means you get the latest advancements in data storage, processing, and analytics without having to invest in data research and development.
- Faster time-to-market – Fully managed data capabilities simplify data management and enable faster application development. This helps your software development process bring new products and features to market more quickly.
How to get started with fully managed data services?
Developing fully-managed data capabilities can be a complex and resource-intensive endeavor. It’s simpler than it’s ever been, but still requires significant expertise in data management. You might be able to reduce headcount, but you still need a solid team.
Here are some of the things you can do to approach fully managed data capabilities realistically.
- Define business goals and requirements – You’ve heard this one before, but it still matters. Before starting on the technical side, clearly define the business goals and data requirements. Identify the key metrics that the business needs to track and the types of insights needed from the data.
- Assess current data capabilities – Evaluate the current state of your data management, including the infrastructure, tools, and processes in place. Identify gaps and areas for improvement that match with your business goals.
- Choose the right managed data services partner– Based on the business requirements and current state assessment, choose a managed data service provider that fits the needs. Consider factors like the type of data to be handled, expected data volume, required scalability, cost, security, and compliance requirements.
- Plan data migration – If data is currently stored in-house or with a different provider, plan for its migration to the new managed service. This can be a complex process, so it may be helpful to engage with experts who know specialized data migration tools.
- Implement data governance and security measures – Set up appropriate data governance policies and security measures. This includes defining who has access to the data, implementing data encryption, setting up network security, and ensuring compliance with relevant regulations.
- Train your team – Ensure that your team has the necessary knowledge to work with the new tools and processes. This could involve training on the specific technologies used, best practices for data management, and data governance policies.
- Monitor and optimize – Once the fully managed data capabilities are in place, continuously monitor performance and optimize as necessary. This could involve scaling up resources as data volumes grow, improving data processing times, or enhancing data security measures.
- Review and iterate – Regularly review the data strategy to ensure it continues to meet business needs as they evolve. This could involve adding new data sources, integrating new tools, or adjusting data governance policies.
Remember, developing fully-managed data capabilities is a significant undertaking that requires a clear strategy, a skilled team, and substantial resources. It’ll save you money in the long run if you get it right the first time around.
But if you haven’t done it before, it’s a good idea to look for a modern application development (MAD) services provider who already knows the process from end-to-end.
How do I find someone I can trust to build fully managed data capabilities?
Your data infrastructure is the foundation of your business and you don’t want to trust it to just anyone. That leads many to try and hire an internal team of DataOps pros but that could take 6-18 months (or longer) to hire and bring them up to speed. That’s way too long for most people to wait and that’s why Codingscape exists. We can help you start migrating your data capabilities to the cloud by next quarter.
We get up to speed faster than other firms (or internal recruiting) to start delivering software you need. Zappos, Twilio, and Veho are just a few companies that trust us to build software. We’ve also built solutions for Amazon and Twilio. We know every layer of data at scale and love to help companies take full advantage of today’s managed data services. You can schedule a time to talk with us here. No hassle, no expectations, just answers.
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Cole is Codingscape's Content Marketing Strategist & Copywriter.