Top Five Big Data Architectures

Given the speed of today’s market, companies that want to stay at the forefront of their industries cannot afford to make mistakes, particularly when it comes to housing and analysis of critical data. As data technology continues to evolve, the once cutting-edge approach of planning data architecture that will last for three to five years is quickly becoming outdated.

Not only should architects update legacy data systems to help their company stay agile, but long-term data strategy is also key to selecting the right architecture. Putting a fluid architecture in place that can quickly adapt to new technology is the way forward for enterprises that want to be agile and succeed in the long term.

How to Choose the Best Data Architecture for You

Understanding big data architecture means knowing what architectures exist and what applications can make them more efficient. Architects not only need to know how to select the best architecture and applications for a particular situation but also how to implement them effectively.

Implementing a well-planned data architecture may take time and money upfront, but it is the best way to reduce costs over time and prepare the organization to adapt to new technologies in the future.

A successful data architecture accounts for all business needs along with the existing data and system requirements. Then, it helps create a flow of data through the enterprise’s systems to optimize business performance. All of this must happen quickly and efficiently to minimize any gaps in the data due to downtime or system errors.

When Choosing Data Architecture, Think New Technology

When modernizing a data solution, incorporating new technology may not be front of mind. However, this is a costly error that allows technical debt to build.

Here are a few concepts that may soon become critical to your organization:

• Data lakes: As the amount of data coming into the average enterprise continues to grow in volume and speed, a data lake helps stop the numbers from overwhelming the system. Centralized data platforms act as a buffer to process transactions without taking computing power from core systems.
• API adoption: The use of an API (Application Programming Interface) automates data going in and out of key applications to ensure real-time numbers are used in the system. API integrations reveal deeper data insights funneled through to the front end for quick implementation.
• Curated data vaults: These can be established per domain as part of the unified data and analytics core. This keeps the data separate until it flows through to the API to empower actionable insights.
• Serverless data platforms housed in the cloud: These platforms offer increased agility and easy, fast deployment to help enterprises get to market as quickly as possible.

Most Popular Data Architectures

These are the five most popular kinds of AWS data architectures:

1. Streaming

A solid streaming setup allows you to minimize spikes in the load that can negatively impact data. This provides a constant, uninterrupted flow of data based on real-time numbers that can be used for accurate analysis.

Real-time data is now everywhere, integrated into everything from rideshare apps to safety sensors, making it crucial for businesses to start using live figures to keep up with the marketplace.

Creating your streaming solution can be cost-prohibitive, leading most AWS users to use an existing offering. Popular choices include Apache Streaming, Graphite, and Kinesis.

2. noSQL

As data volume and variability can make it harder for engineers and architects to manage the information manually, a noSQL engine helps stabilize data models while increasing their accuracy. A wealth of third-party systems exists to automate provisioning and backups, reducing the amount of time needed to manage the data and overall costs of unnecessary historical data storage.

noSQL engines are designed to balance concurrent workloads coming from disparate sources, including the end-user and IT team. The easy scalability of these engines makes them ideal for use in real-time applications while keeping latency low.

Graph databases allow for data modeling to help manage data that is constantly changing and evolving, which is especially useful when an enterprise begins incorporating AI into specific data layers.

3. Analytics

Managing the volume of analytics information coming into the average enterprise is a significant challenge that many IT departments identify as a top concern. The time savings that in-place analytics offers to data architects is significant, allowing them to devote energy to more critical tasks.

Operating with in-place analytics cuts down on the cost of storing this data in a separate cluster and allows for easy access. This makes it more convenient to perform simple searches by querying data across the organization to find fast answers. Tools like Amazon Sagemaker and Kubeflow are ideal for simplifying the creation of an end-to-end analytics solution.

4. Batch Clusters

Running batch workloads on the AWS cloud delivers faster results and removes the need for infrastructure management by separating storage from computing.

Clusters offer a more affordable option for generalized storage to automatically distribute the workload and reduce the need for batches through resource provisioning. Some tools and applications can monitor the cluster and alert your team of issues instead of needing someone to actively watch them.

These clusters may perform the functions of the other architectures detailed here as a backup if needed. Cloudera and Amazon EMR (Elastic MapReduce) are major players in this space. EMR allows for integration with Apache and other open-source tools so that repetitive tasks can be automated.

5. Enterprise Data Warehouse (EDW)

Running a unified data analytics engine based on EDW hardware setups allows cost-effective, large-scale data processing. This option also allows for faster analytical processing across your organization.

In the days before widespread cloud adoption, EDW setups were cost-prohibitive for most businesses. Thanks to the changes in the digital landscape over the last 20 years, EDW and OLAP (online analytical processing) can now be accessible to smaller, up-and-coming enterprises. The OLAP nature of an EDW can prioritize the needs of end-users, even if they are complex and data-heavy in nature. This allows your IT department to focus on other, more pressing tasks.

Amazon Redshift is a solution that lets smaller players scale their EDW needs at a moment’s notice, making it ideal for new users who are unsure about how their data needs might evolve.

Achieve Your Goals with the Right Data Architecture

Every enterprise using AWS must decide which solutions it can combine to set up data architecture that delivers insights into the high volume of information collected.

These five kinds of AWS data architectures are simply a starting point to help your IT department or data architects provide a full framework that offers robust insights into your business. More solutions are constantly emerging to help make the management of a big data architecture simpler.

Related Courses

• Big Data on AWS
• Data Warehousing on AWS
• Building a Serverless Data Lake