Digitization is accelerated by the pandemic, so business processes can be monitored, stored, analyzed, and acted upon. This elevates the importance of the data stack and its management. Now, it is business-critical to be cost-efficient with data and be able to convert it to insight.
As data has grown into a valuable asset for all organizations, data management is becoming vital as well. The practice of managing and optimizing the flow of data within an organization is called data operations or DataOps. This includes everything from the initial data collection and storage through processing and analysis to its ultimate use and disposal.
With the growth of big data, organizations are now dealing with large amounts of data that need to be stored and managed efficiently. The optimization of data storage is one key aspect of DataOps. To ensure that data is stored as efficiently as possible, the use of advanced technologies is required. By using techniques like data compression and deduplication, organizations can store more data in less disk space, which can save money on storage hardware and other associated costs.
In addition to cost savings, data storage optimizations can also improve performance and reliability. By storing data in a way that is optimized for fast access and retrieval, organizations can improve the speed and responsiveness of their applications and services. This can be especially important for real-time applications that require quick access to data, such as those used in financial trading or real-time analytics.
Another important factor of data storage to consider is the environmental impact. As the amount of data being generated and stored continues to grow, the energy consumption and carbon emission associated with data centers are becoming significant concerns. By optimizing data storage, organizations can reduce their energy usage and lower their carbon footprint. This not only helps to protect the environment but also improves the sustainability of their operations.
In conclusion, DataOps and optimizations are critical for maximizing the value of data in organizations. By implementing effective data storage optimizations, organizations can save money, improve performance and reliability, and reduce their environmental impact. As the importance of data continues to grow, the practice of DataOps and the use of optimizations will only become more essential.
Let me quote one of the investors from Menlo Ventures on the subject:
Venky Ganesan: The digital transformation that was happening just got super accelerated by the pandemic. All these analog business processes were digitized. And now that they are digitized, they can be tracked, stored, analyzed, evaluated and acted upon. I think the data stack has got to be one of the most important stacks in a company because your success long term is going to be based on how good is your data stack? How good is your DataOps? And then how do you build the analytics on top of it?
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This is exactly why we created Depoxy: to help organizations get the most out of their data. We try to make their system more efficient, starting from the storage level all the way up to the more advanced processes like analytics and machine learning.
This is an extremely early release, as our goal is to capture your imagination and rely on your feedback to implement features that deliver the most value to our customers.
These are the initial features we have in mind:
- UI for managing data pipelines (jobs, queries, databases, tables) DONE
- Metadata analysis for optimizing data in storage (MetadataTool) TBD
- Data lineage based on queries (Queryparser) TBD
The first feature – which we implemented for the AWS data stack – is a slick UI for having databases, tables, queries, and ETL jobs in one place. By reducing the time spent on accessing metadata or finding out what happened to an operation, it already provides value to our early users.
The second feature is what we are working on at the moment: a metadata analysis engine that visualizes how data sits on disk. It helps data engineers to make decisions about what to optimize and how. Reducing the size of data to achieve better efficiency – by better structure, compression, or other means – can be a game changer both in terms of cost and CO2.
We know the following about CO2 in the context of data storage:
A Carnegie Mellon University study concluded that the energy cost of data transfer and storage is about 7 kWh per gigabyte. An assessment at a conference of the American Council for an Energy-Efficient Economy reached a lower number: 3.1 kWh per gigabyte. (A gigabyte is enough data to save a few hundred high-resolution photos or an hour of video.)
Compared with your personal hard disk, which requires about 0.000005 kWh per gigabyte to save your data, this is a huge amount of energy. Saving and storing 100 gigabytes of data in the cloud per year would result in a carbon footprint of about 0.2 tons of CO2, based on the usual U.S. electric mix.
For more details, read the whole article.
Luckily, if we optimize the size of data on disk, we achieve three things:
- lower cost
- faster query execution (analytics or machine learning)
- reduced CO2
This is why we believe our tool has a real potential to become invaluable for any data-driven company.
The third feature is a data lineage generator that leverages SQL queries run on the system and catalog information to automatically generate data lineage visualization. This helps data engineers and scientists understand where the data is coming from and where it is flowing to.
The pandemic accelerated the digitization of business processes, which has elevated the importance of the data stack. As the significance of data continues to grow, DataOps and optimizations will only become more essential. Depoxy can help organizations to get the most out of their data throughout their system: from storage to analytics and ML. By data storage optimizations, organizations not only can save money, but also improve performance and reliability, and reduce their environmental impact.
Let us know what you think.
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