An open data lakehouse can help maximize the value of an organization’s digital transformation pipelines while bringing together open-source systems and the cloud. This setup merges the best of data warehouses and data lakes by offering storage for any data type suitable for both data analytics and machine learning (ML) workloads. It is cost-effective, fast, and flexible, and provides a governance or management layer that ensures enterprise operations’ reliability, consistency, and security.
Using open-source technologies and standards like PrestoDB, Parquet, and Apache HUDI reduces license costs and ensures that critical systems are continuously developed by companies that use them in production and at scale. By shifting data from a cost center to a profit center and employing an open data lakehouse in operations, organizations can increase the chances of their data ecosystem paying dividends.
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Organizations that implement an open data lakehouse can significantly enhance the value of their data ecosystem. Despite the widespread belief that data can unlock productivity and improve competitiveness, it is often viewed as a “cost of doing business.” This mindset results in organizations searching for cheaper ways to extract value from their data, which typically involves outsourcing to the lowest bidder. However, treating data and its associated systems and people as business assets can lead to greater returns on investment.
One way to do this is by making your data useful to customers and clients by exposing curated versions of it, such as through dashboards. By doing so, you can charge them for access, and your data can start earning its keep. Additionally, using the cloud vendors’ low-cost, high-availability object stores and robust built-in security frameworks makes this task much more cost-effective than before.
However, an open data lakehouse can provide even greater value. It allows organizations to store data in its original, unaltered format, enabling various data consumers to access and analyze it in real-time. This results in quicker and more accurate data insights, leading to faster and better decision-making. Moreover, an open data lakehouse facilitates collaboration between different teams and data consumers, leading to more innovative solutions and ideas.
By utilizing these strategies, organizations can prevent their data from going to waste and instead turn it into a valuable business asset. Implementing an open data lakehouse can be a significant step towards achieving this goal.
The concept of “openness” might seem daunting to some, with many people associating it with being unprotected or unmanageable. However, given the rapid pace of technological advancements, the benefits of openness far outweigh the drawbacks. Some of these benefits include the lack of vendor lock-in, which can save a considerable amount of money over time, as well as the flexibility to adopt and discard technologies and solutions as needed.
In terms of data and databases, an open data format combined with an open-source query engine can offer the reliability and performance of a data warehouse, the flexibility and better price/performance of a data lake, and the freedom of non-proprietary SQL query processing and data storage. This setup also provides the governance, discovery, quality, and security required for effective data management.
Unlike the 1970s, when only a few SQL-based relational database management systems were available, companies now have numerous options that are not tied to a single vendor. By separating storage and compute, data lakes enable organizations to piece together a solution that maximizes the amount and types of data used. Furthermore, they allow for machine learning and AI capabilities in addition to SQL processing, making data lakes flexible, scalable, and cost-effective.
However, data lakes can be disorganized and challenging to manage due to their flexibility, and data consistency issues can make enforcing reliability and security difficult. In comparison, a data warehouse operates like a group of sled dogs tied together and moving in the same direction, while a data lake is more akin to a menagerie of various breeds of dogs running around in different directions.
To effectively manage data lakes, organizations should prioritize data governance and management, including enforcing data consistency and quality, as well as implementing security protocols. By doing so, they can leverage the benefits of openness while mitigating its potential drawbacks.
Databases are known for their scalability, but they still face cost issues as data storage is linked with compute. As a result, processing and cloud infrastructure costs increase alongside data growth. Additionally, managing these complex systems requires a large IT team and numerous data centers. However, employing an open data lakehouse can help maximize the value of an organization’s digital transformation pipelines while bringing together open-source systems and the cloud.
A data lakehouse merges the best of data warehouses and data lakes by offering storage for any data type suitable for both data analytics and machine learning (ML) workloads. This setup is cost-effective, fast, and flexible, and provides a governance or management layer that ensures enterprise operations’ reliability, consistency, and security. Using open-source technologies and standards like PrestoDB, Parquet, and Apache HUDI reduces license costs and ensures that critical systems are continuously developed by companies that use them in production and at scale.
By shifting data from a cost center to a profit center and employing an open data lakehouse in operations, organizations can increase the chances of their data ecosystem paying dividends. This move is crucial as organizations have already invested significantly in data transformation initiatives to remain competitive and drive long-term success.
Joining the DataDecisionMakers community is a great way to stay up-to-date on cutting-edge ideas, best practices, and the future of data and data tech. Experts, including technical personnel doing data work, can share data-related insights and innovations. Contributing an article of your own is also a possibility.
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