Bacalhau Over Traditional Data Processing
Bacalhau Over Traditional Data Processing
(03:15)
Data is being generated at a consistently increasing rate. The International Data Corporation (IDC) projects 175 zettabytes of data by 2025 and 291 zettabytes of digital data generated in 2027. A significant portion of this data is generated in Edge locations, created away from data centers, across many devices, regions, and clouds.
Your businesses and organizations must derive insights from these massive amounts of data while efficiently maintaining and processing it. Traditional data processing systems, focused on centralizing data for analysis, model creation, or other downstream processes, are straining to meet modern business expectations while costing a fortune in time and money.
Bacalhau, an Expanso initiative, enhances data processing and management processes to better deal with distributed data needs and more effectively meet business demands. Bacalhau enables you to bring your computing power to your Edge and distributed resources and transfer only necessary, already processed data to its final destinations.
In this article, you will explore Bacalhau, its capabilities, benefits, and real-world applications. You will especially learn the areas in which traditional data processing solutions are failing and how extending Bacalhau into your infrastructure can help you move and build faster with more security and fewer associated costs.
Traditional Solutions for Data Processing
Businesses and organizations have effectively relied on traditional methods to manage and analyze their Big data. These methods typically involve centralized processing in data warehouses or lakes, where raw data is aggregated and processed in a single location. However, business considerations have grown; about 20% of data is generated in the cloud, around 60% of corporate data is stored in the cloud, and workloads have diversified with new applications for distributed and decentralized resources (streaming media, the Internet of Things, virtual reality and connected cars). All these use cases require large amounts of data delivered with very low latency.
Your data has become too big and too distributed to process efficiently with traditional methods. As your data volume increases, building and maintaining data storage becomes more expensive, requiring significant infrastructure investments. You also have latency issues as your big data moves too slowly as it is transferred to and from centralized locations for processing. This can result in delays and bottlenecks, especially when dealing with large volumes of data or use cases with real-time processing requirements.
Your traditional data processing methods, with their centralized nature, increase your attack surface, making it a prime target for cyberattacks and data breaches. There are more access points to target your data in transit from your various data sources and data at rest within your centralized storage.
Advantages of Bacalhau
Bacalhau offers a unique platform for your data processing and compute tasks in general. It presents an efficient method to execute your processing, analysis, machine learning, or other computation data tasks right where your data is being generated and stored. The Compute Over Data approach simplifies your data management and accelerates big data processing, by ensuring raw data does not need to be transported away from where it is generated and stored. Let's take a look into the key advantages Bacalhau brings to the table:
Cost Efficiency: Bacalhau significantly reduces expenses associated with data processing by minimizing data transfer costs and orchestrating jobs that leverage existing infrastructure. Unlike traditional methods that often involve costly data transfers to centralized locations, Bacalhau processes data locally (wherever your data is generated or stored), maximizing cost savings for your businesses and organizations.
Enhanced Performance: By processing data locally and only transporting results, Bacalhau alleviates network latency issues commonly experienced with traditional methods. This results in improved speed and responsiveness of data processing tasks, enabling your organizations to analyze data in real time and make timely, data-driven decisions.
Better Security Profile: Bacalhau prioritizes security by keeping data in place, reducing the risk of breaches and data leaks associated with data transfers. By minimizing the exposure of sensitive data to external networks, Bacalhau enhances data security and compliance, ensuring the confidentiality and integrity of enterprise data.
Real World Examples
Let’s explore a few real-world examples that demonstrate the power and effectiveness of Bacalhau in various industries and use cases. These examples will showcase how Bacalhau helps you overcome challenges and inefficiencies in your infrastructure while achieving significant benefits in terms of cost savings, performance improvements, and enhanced security.
Maintaining the security of your distributed fleets: Here, we see Bacalhau being used to implement monitoring on your fleet network. This process enables full visibility and observability of the operations within your network, boosting your security, and empowering quick debugging and recovery.
Edge-based inference machine learning: Your key data for model training and inference (video, text, etc.) are generated on devices and at locations away from your centralized data and ML model stores. With Bacalhau, you can extend your ML training to where the data is, shifting ML inference away from central hubs and generally cutting costs and boosting efficiency while delivering real-time insights.
Real-time monitoring and log analysis: Bacalhau’s more efficient data processing method can be applied toward observability and the real time analysis of your system metrics and logs across distributed resources. Using deployed Bacalhau jobs, you can automate Error Spike Analysis, Traffic Source Monitoring, Resource Usage Analysis, and Security Checks directly on your distributed application, only transferring triggers, alerts, or results to your central hub.
Conclusion
Traditional data processing methods are failing to keep up with the large volumes of essential data being generated from diverse sources. The need to process your raw data to a centralized location before any fundamental analysis or value can be extracted presents inefficiencies in your infrastructure. In this article, you explore Bacalhau as an alternative solution, walking through its cost, performance, and security benefits.
