The challenges of ever-growing Data
Data Engineering on Cloud — II
As datasets continue to grow, the challenges become more complex. Initially, storing vast amounts of data was a major hurdle. Simply having enough space to keep all the data was difficult, and as the data grew, finding and managing it efficiently became a significant issue. Processing speed also emerged as a crucial factor, as larger datasets took much longer to analyze and use effectively. Managing and organizing these datasets became increasingly difficult, and the systems needed to handle them required substantial scaling capabilities and cost management.
The journey to address these challenges began with file systems, where data was stored in simple files on physical storage devices. This approach was straightforward and easy to implement, but as data grew, it became slow and inefficient for searching, retrieving, and organizing information.
To overcome these limitations, the industry moved to databases, which provided a structured way to store data using tables, rows, and columns. This structure made data management and querying more efficient, thanks to SQL (Structured Query Language). However, as the amount of data continued to grow, traditional databases struggled with scalability and handling very large datasets. They required expensive hardware and were not easily adaptable to growing demands.
The introduction of data warehouses marked a significant improvement. These systems collected and organized data from multiple databases, facilitating complex analysis and reporting. Data warehouses improved data retrieval speeds and enabled better decision-making by consolidating information in one place. However, they were still limited by high storage costs and scalability issues. Additionally, data warehouses were primarily designed for structured data, making it difficult to handle unstructured data like text, images, and videos.
To address these shortcomings, the industry developed data lakes, with Hadoop being a key player. Data lakes allowed for the storage of vast amounts of raw data in its native format, whether structured or unstructured. This flexibility, along with improved scalability and lower storage costs, made data lakes an attractive solution. However, Hadoop’s processing model, MapReduce, was slow for real-time analytics. Moreover, managing and securing data in data lakes was complex, and ensuring proper data governance was challenging.
The introduction of tools like Apache Spark significantly improved data processing speeds. Spark’s in-memory computing capabilities allowed for much faster analysis of big data, supporting both batch and real-time processing. However, even with these advancements, challenges remained in integrating data from various sources, ensuring data quality, and maintaining robust data governance.
As cloud technologies emerged, they provided a new paradigm for handling large datasets. Cloud-based solutions offered scalable, cost-effective storage and processing power on-demand. They further improved the flexibility and speed of data management, enabling more efficient handling of ever-growing datasets. However, even in the cloud, challenges like data integration, quality assurance, governance, and real-time processing persist. Ensuring data privacy and security in these vast, distributed systems continues to be a concern, and balancing the costs of storage and processing with the benefits of data analysis remains an ongoing challenge.
Data mesh is a new approach to handling large datasets that aims to address some of the remaining challenges by decentralizing data management. Instead of having a centralized data team, data mesh distributes responsibility to different teams within an organization, allowing them to own and manage their own data domains. This approach helps improve data quality and governance because the teams closest to the data understand it best. It also enhances scalability, as each team can work independently and in parallel. Data mesh promotes a more flexible and efficient way to manage data, making it easier to integrate different sources and respond to real-time needs, but it also requires strong collaboration and communication across teams to be successful.
In summary, the industry has made significant strides from file systems to modern cloud-based solutions, each step addressing key issues of storage, processing speed, and scalability. Despite these advancements, the rapid growth of data presents ongoing challenges in integration, quality, governance, real-time processing, and cost management.
If you loved this story, please feel free to check my other articles on this topic here: https://ankit-rathi.github.io/data-ai-concepts/
Ankit Rathi is a data techie and weekend tradevestor. His interest lies primarily in building end-to-end data applications/products and making money in stock market using Tradevesting methodology.
