Moving Seamlessly from Development Environments to Production RealitiesMoving Seamlessly from Development Environments to Production Realities

Moving Seamlessly from Development Environments to Production Realities

March 22, 2023
Alireza Farhidzadeh

Moving code from development to production can be complex and time-consuming for many data engineers. Even after spending hours experimenting with data sets inside notebooks, getting that code ready for production can still be difficult. The code needs to be reviewed, tested, and optimized for performance, which can involve reworking and refining the original code. The challenge is ensuring that the code can still capture the insights discovered during experimentation while meeting the stringent requirements for production environments. 

This is where Bodo comes in, offering features that enable data engineers to streamline the process of moving from experimentation to production. Bodo is uniquely equipped to handle very large datasets, including terabyte-scale data and enables easy experimentation through notebooks. With Bodo's just-in-time inferential compiler, data engineers can take advantage of parallel execution to speed up experiments and make use of even the largest datasets.

When an experiment is successful, translating it to a production job often poses a bottleneck to the otherwise rapid-fire development cycle that notebook experiments enable. Therefore, you must streamline the translation from conceptual experiments to working production features.

Moving from Development to Production

Data engineers and data scientists often experiment with enormous, unwieldy datasets. Such a complex task requires highly optimized code and a high-performance computing (HPC) engine. You must rewrite the code for optimization if the code is in a high-level language. 

Python is a popular language, known for its simplicity — but not for its multiprocessing capabilities. That's because it doesn't scale with increasing data volumes. Python processes typically use a single thread because of the global interpreter lock (GIL). With massive amounts of data, your program must be rewritten into a low-level language or implemented by parallel programming experts to take full advantage of the cores.  

Code conversion is time-consuming and requires switching from one language to another. The Bodo Platform’s data processing capabilities enable you to do this while staying within the Python environment. It's a solution that uses Python's simplicity and HPC's performance and scalability by removing the complexities of moving from development to production. Bodo enables you to generate optimized parallel Python code. 

Efficient Large-Scale Data Processing with Bodo

Bodo’s parallel computing platform provides high-performance and efficient large-scale data processing through an optimized compiler and a parallel runtime system. A just-in-time (JIT) inferential compiler analyzes code from high-level languages and generates parallel machine code without manual engineering.

With Bodo, you can speed up data processing workflows linearly with the number of cluster cores and save up to 95 percent of the costs without converting your code into another language. It also lets you execute programs far more efficiently and scale predictably. You can achieve linear performance from your favorite Python libraries past ten thousand cores.

Bodo processes workloads far more efficiently than alternatives, requiring more than 90 percent fewer hardware resources (CPUs and memory) for a given workload. This efficiency reduces the capital costs of infrastructure and energy. Moreover, it supports green initiatives by cutting operational and energy consumption costs. With Bodo, you can achieve simplicity, performance, and efficiency.

Using Bodo with Notebooks

Bodo provides complete parallelism using the single program multiple data (SPMD) paradigm to achieve multi-node parallelization and process data faster over many nodes. It’s a subcategory of the multiple instruction, multiple data (MIMD) technique which splits up tasks to be run concurrently on multiple processors to obtain faster results.

With Bodo, you can parallelize your code on a cluster of machines rather than multiprocessing on a single device. Bodo on a single device is also faster than other available multiprocessing libraries. This way, you take advantage of as many nodes as possible. Its linear scaling capability handles jobs involving terabytes of data.

Bodo offers an authentic parallel architecture using a Message Passing Interface (MPI) and generates native parallel machine code binaries. This helps data engineers who want to bring their developed code into a production environment at a larger scale. 

Running analysis on large amounts of data helps data scientists gain more profound insights ​​because this amount of data must come from a large variety of data sources. Diverse data sources help address data biases, skewed values, overweighted values, and underrepresented data. It helps train unbiased ML models because using more data results in better accuracy, ultimately fueling better, faster decision-making.

So Bodo’s ability to work with extensive datasets gives you the power to execute analytics jobs faster and dive deeper into experiments. You can put your notebooks into production as standalone applications with dashboards. You can also use notebooks to schedule jobs over the cloud, serving as web pages to end-users.

Turning Notebooks into Production Jobs

Turning notebook experiments into production jobs can be a nightmare for data engineers. It requires multiple stages of engineering effort to build a complete ML infrastructure for large-scale production. You must think about handling your data preparation and the extract, transform, and load (ETL) workload. You must also ensure that your notebooks are reproducible and testable.

Since you must switch between development and production frequently, you should have a proper continuous integration and continuous deployment (CI/CD) pipeline set up where all project components are versioned, including the code, data, and the model’s metadata and its attributes. Last, you must optimize your code when the size of your data grows, which may require switching between various programming languages like Python.

You must invest many resources to support a workflow where you can translate notebook experiments into a production environment. Data scientists often develop proof-of-concept models with limited data and computing using Python because of its ease and simplicity. Then, to optimize the code for large amounts of data and high performance, additional teams step in to add new frameworks for code parallelization or completely rewrite the code in another language.

Bodo eliminates the need to build a complex ML infrastructure. You can keep your entire DataOps and MLOps pipelines in Python. You don’t have to use a different language or library for high performance. 

Deploying and Monitoring Jobs

Bodo’s UI makes deploying, managing, and monitoring jobs running on notebooks or in production simple. It provides connectors and integrations that allow you to connect with terabytes of data within minutes. Bodo’s integrated workspace with multi-cloud support makes it easier to monitor cloud resources by displaying information in one convenient place.

You can use the control plane to deploy data transformation jobs using job scheduling and Bodo’s SDK and turn your interactive notebooks into production jobs. You deploy experimental notebook jobs on dedicated job clusters from a simple job management UI. You can add fine-grained resource control to manage your compute costs, such as pausing and scaling clusters. 

Parallelization Across Python and SQL

We’ve added a SQL engine on top of our specialized Python compiler that uses the same technology to parallelize and accelerate workloads. It provides a complete solution for data engineers to move their notebook experiments to production environments. By allowing for end-to-end optimization, type checking, error checking, and parallelization across Python and SQL, Bodo eliminates the need for major code changes or additional developer training. This streamlines the process of transitioning from experimentation to production.

One of the key benefits of using Bodo during experimentation is its parallel architecture, which uses MPI for execution. This is particularly important for data engineers experimenting with large datasets in notebooks, as they can transition their code to Bodo for production without experiencing performance degradation. 

Bodo's parallel architecture ensures the engine can scale efficiently as datasets and core counts grow. It allows data engineers to experiment with large datasets and quickly move their code to production environments.


Notebooks enable rapid experimentation with data sets and significantly speed up development cycles. However, translating these into production jobs often poses various bottlenecks. Bodo eliminates the need for building complex infrastructure to manage your DataOps pipelines and makes it easy to turn notebook experiments into production jobs. 

With Bodo, you can parallelize your code on a cluster of machines rather than multiprocessing on a single device. It unlocks opportunities for large-scale analytics, data science experiments, and production. Data scientists can take advantage of the simplicity of Python without worrying about performance degradation. Bodo delivers the performance and scalability of HPC and enables writing code that you could move to production immediately.

Bodo provides a simple, easy-to-use UI to deploy, manage, and monitor jobs running on notebooks or in production. It lets you connect with terabytes of data and use job scheduling to turn your interactive notebooks into production jobs. 

Bodo’s optimized compiler and efficient parallelization give high performance and efficiency when analyzing big data, saving you the trouble of creating a complicated ML infrastructure. You can also keep your whole DataOps and MLOps pipelines in Python and notebooks. 

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