Every moment, all across the world, an incomprehensible amount of data is being created, processed, and analyzed. At the same time, these activities themselves generate even more data in the form of events, metrics, logs, and more — which in turn require even more processing and analysis. The cycle is endless.
With all this data, it's no wonder that businesses and individuals are constantly looking for ways to get more out of it. The faster you can process and analyze the data available to you, the more intelligent and agile you will be. The more you can leverage the inherent value in this data, the better you will be able to outperform and outmaneuver your competitors.
This reality has ushered in the rise of data-driven culture.
Today, users and firms increasingly rely on data to guide their workflows and processes. Organizations of all sorts are moving toward a seamless and holistic approach to data operations and using data to inform decision-making.
Given all this, it should come as no surprise that the data pipelines responsible for moving the data — from where it is generated to where it is required — have become almost as critical as the data itself. The less time and energy spent moving data around, the more you can concentrate on the tasks that actually drive value.
Data-driven workflows and processes, however, are not without their challenges.
But there is good news: These challenges can be overcome by adopting the appropriate solutions and integrating them to provide a comprehensive data operations platform.
Data Workflows Challenges
One of the biggest challenges in adopting data-driven workflows and processes is the variety, velocity, and volume of our Big Data environment. The data needs to be ingested, processed, and analyzed as fast as possible. The variety of data results in separated systems that somehow need to communicate. It further leads to the problem of siloed data that requires integration. The volume and velocity of data needs systems with high performance, reliability, and scalability.
Different users normally have different data needs while performing their tasks. They may adopt different approaches for their data operations, leading to different applications that need to be integrated. Creating data-driven architectures and applications is time-consuming and expensive. And it requires engineers with different skills.
All the these factors make creating data workflows difficult — and sometimes unfeasible for smaller teams.
Creating Solid Data Operations
To get the most out of your data, you need solid data operations built alongside enterprise-ready data pipelines and workflows. A holistic, seamless, and simple approach is needed to solve problems like data integration, data orchestration, data management, and solution deployment.
How do you actually know if your data operations are solid? To truly know, you might have to live and breath this world every day, but the following characteristics represent six telltale signs of a good approach — and Shipyard can help you check all the boxes.
1. Easy Integrations with your Data Stack
To get the best out of your data operations, you need a platform that integrates well with various databases, data storage services, messaging platforms, and SaaS applications. This helps solve the problem of "siloed" data (that can't be accessed easily) and smooths out the creation of data-driven workflows.
Inevitably, the platform you choose will not have 1 or 2 integrations that your businesses desperately needs to see the big picture with your data. In those, cases you'll need to have the flexibility to create your own integrations with code.
2. Clear Visibility and Reproducibility
Data operations need to be easily managed and monitored. If something goes wrong, you need to know exactly what happened in the moment so you can effectively troubleshoot the issue and get things back on track. When issues arise, you need to know what changes have been made to the code or the data so you can effectively reproduce the problem. There's nothing worse than the occasional unexplainable "blip" in your pipelines.
You also need the peace of mind that if one process upstream runs into errors that there won't be any downstream effects. Otherwise you might be on clean-up duty for weeks, fixing up incorrect dashboards, faulty reporting, and poorly trained ML models.
3. High Availability and Low Latency
For data operations to be useful, they have to be available on-demand and execute as quickly as possible. Any team member should be able to add and subtract tasks from a workflow when needed. Jobs shouldn't get stuck forever in a queue, delaying data delivery due to poor infrastructure management. When necessary, jobs should be able to run in parallel and scale to meet operational demands. By focusing on availability and low latency, you can ensure that your organization has the ability to scale analytics at a moments notice and quickly use new data to increase competitive advantage.
4. High Performance and Throughput
Processing millions of rows of data is challenging, especially for small teams and firms. At a certain point, your personal laptop or cron jobs just won't cut it any more.
To effectively build for an increase in data size and complexity, you need to ensure that your data operations platform isn't running into memory and storage constraints. Scaling dynamically based on the load of data being worked with is imperative to ensuring that your data operations are able to grow at the same rate as your business.
At the same time, you also need your database to effectively handle hundreds of queries being thrown at it each hour. If you're not already using a cloud database like Snowflake or Bigquery, it's highly recommend to avoid the headaches of scaling down the road.
5. Robust and Resilient Error Handling
Given the importance of data operations to other processes in a data-driven environment, failure of critical infrastructure and data pipelines not an option. However, data teams shouldn't be responsible for covering every error that's ever possible in their code. You need to have a system that's highly resilient and robust. It should be able to automatically flag errors, recover from failure with retries, manage virtual environments, prevent jobs from getting stuck, and meet unpredictable processing needs.
The end goal is to spend more time writing business logic in your code, while offloading the responsibility of resiliency to the data operations platform you choose. With this setup, you spend less time focusing on error-management while ensuring your business processes can recover from any failure quickly, making it easier to sleep well at night.
6. Flexible Scheduling
Your data operations are constantly evolving, requiring the platform you choose to allow you to run pipelines in myriad ways. Team members should be able to manually run processes on a one-off basis or build a detailed, consistent schedule for execution. Some processes may only need to be run after specific events occur, making the need for webhooks even more important. In many instances, you may also want to have error-resolution processes run alongside your success-based workflows, so your team can proactively manage expectations with data issues without having to lift a finger. What's important is having the flexibility to choose how your data pipelines run at any given moment to match the needs of your organization.
7. Ease Of Use and Quick Onboarding
Ease of use is critical when building a data-driven culture. Any company can have one or two engineers in control of everything, but allowing the data to flow across the business is where the real value lies. When building data pipelines, you should strive for a solution that reduces the steps to be "production ready" while offering easy ways to share processes with others in the organization.
Leveraging the Power of Big Data
When you add it all up, there is definitely a lot to keep an eye out for. But it doesn't need to become a Herculean task. The right approach and the right solutions make it much easier to get up and running — and keep your data operations humming along for years to come.
Big Data isn't only for the big boys anymore. Now, after years of advances and infrastructure improvements, it's easier than ever to leverage the power of all this data. Using a data operations platform like Shipyard can help you operationalize your data with ease, giving your organization a huge competitive edge.
Ready to get started? Try Shipyard today with our free Developer Plan to get started building robust, resilient pipelines in a matter of minutes.
About Shipyard:
Shipyard is a modern data orchestration platform for data engineers to easily connect tools, automate workflows, and build a solid data infrastructure from day one.
Shipyard offers low-code templates that are configured using a visual interface, replacing the need to write code to build data workflows while enabling data engineers to get their work into production faster. If a solution can’t be built with existing templates, engineers can always automate scripts in the language of their choice to bring any internal or external process into their workflows.
The Shipyard team has built data products for some of the largest brands in business and deeply understands the problems that come with scale. Observability and alerting are built into the Shipyard platform, ensuring that breakages are identified before being discovered downstream by business teams.
With a high level of concurrency and end-to-end encryption, Shipyard enables data teams to accomplish more without relying on other teams or worrying about infrastructure challenges, while also ensuring that business teams trust the data made available to them.
For more information, visit www.shipyardapp.com or get started for free.
