A New Platform for Data Applications
We’re building a platform that makes it easy for developers to build, deploy, and scale data-intensive applications. Our open-source project, Aineko, provides building blocks that make it simple to create prototypes of high-performance data apps that run on your laptop. Aineko Cloud transforms your prototypes into production applications without changing any code.
We want developers to focus on transformations and business logic, not lose sleep over infrastructure and the maintainability of their codebase. With Aineko, you can process immense amounts of data in real-time from anywhere and make your results available via interactions, APIs, web-hooks, or anywhere else you can think of. Build inference pipelines, custom trigger-based workflows, and always-fresh APIs all within a single Python framework.
Our Journey
When we commenced building Aineko as an internal tool, a familiar challenge emerged. We needed to ingest a lot of data and make real-time decisions based on transformations and business logic. Throughout our careers, we encountered recurring problems of the same nature. The solutions led us to build what we now term data-intensive applications.
Whether it's immediate decisions on IOT devices, a signal-based trading application, a real-time product analytics suite, or optimization of supply chains, the primary challenge was managing “the 3 V’s”: volume, velocity, and variability of data.
Implementing these applications required significant effort, but the greatest challenges arose from ingesting and processing petabytes of data with millisecond latencies and accommodating evolving data schemas. Obtaining and managing all the data necessary for applications to make informed decisions in real-time demands considerable investment.
In conceptualizing Aineko's design, we established some maxims related to building data-intensive applications:
All data is streaming in nature; batch is simply lower on the spectrum.
Pipelines often contain components that can be re-used in subsequent ones.
While microservices offer advantages, monoliths excel for smaller teams.
The simplicity, adaptability, and ecosystem of Python trumps the capabilities of SQL.
Most application backends are essentially data pipelines.
Developers prioritize features over infrastructure management.
Production environments never perfectly replicate development when it involves data.
Distributed actor frameworks resonate with human problem-solving approaches.
Connectors, business logic, inference, transformations, interactions, and governance constitute the building blocks of a modern data stack.
Guided by these principles, we built the initial version of Aineko as an internal tool. It empowered our team to swiftly develop prototypes and expand production systems stress-free. Recognizing the internal advantages of Aineko, we pondered, "Why not share Aineko with the broader developer community?" We then decided to concentrate entirely on building Aineko and Aineko Cloud as a tool for all developers.
Why Aineko is Important to Us?
Advancements in the developer experience have enabled software engineers to build astounding applications that significantly influence our daily lives. Platforms like Stripe, Slack, Shopify, and Uber benefited from modern development tools. Though these tools launched with lean tech stacks, they've since evolved into vast systems that are challenging to refine and costly to maintain. They cater to millions of users, often at the expense of innovation.
We believe that a major reason for this rift is that modern tools do not account for the interplay between compute and data. Data app development is ripe for transformation. Just as Vercel and Netlify democratized web app prototype development, we envision Aineko simplifying the process for developers building data-centric applications, without compromising scalability.
We are experiencing a data deluge. Every day innovative apps are conceived using the petabytes of data generated online. Despite this, data apps have yet to bask in the limelight. While platform teams combine tools from various vendors or build bespoke in-house tools, a unified product that integrates data and compute to genuinely enhance the development experience remains elusive. We predict that every software engineer will soon be a data engineer. If we succeed, they might not even realize it.
How Does It Work?
Our goal with Aineko is twofold: simplicity for novice Python developers to build prototypes and flexibility for experienced developer teams to architect complex applications. This dual aim is realized through practical abstractions. These, when combined in diverse configurations, embody your application's core functionalities, guiding developers with best practices to avoid future scaling issues, while retaining the flexibility inherent to Python scripts.
Apps built using the Aineko framework inherit two invaluable traits. Firstly, they can operate locally (akin to npm), obviating the need for weaving together infrastructure needed to guarantee high performance, thereby increasing development velocity. Secondly, they are deployable on Aineko Cloud. Once hosted on Aineko Cloud, applications are automatically equipped with CI/CD, infrastructure management, and observability tools, ensuring smooth operation and scalability.
We envision Aineko Cloud becoming the go-to platform for data-intensive applications, revolutionizing the developer experience by drastically simplifying the creation and management of data apps.
What Can You Build?
We've built fascinating use cases on Aineko, such as a code generation pipeline leveraging generative AI, an always-fresh API consolidating data from numerous blockchains, a real-time notification suite compatible with various messaging apps, and a low-latency algorithmic trading system. In our upcoming posts, we'll illustrate how you can leverage Aineko to build these use cases and more.
We’re excited to see what you will build with Aineko...