As data becomes the new fuel, companies across sizes and sectors have begun collecting large amounts of data from all sources possible – in the hope of unearthing insights previously unheard of. While it is true that data powers every business decision today, storing this snowballing amount of data hasn’t been easy.
While data warehouses and data lakes serve as ideal repositories for storing raw data in different formats, both rely on different processes for capturing data and each comes with its own set of boundaries around specific transactions such as data quality controls, batch processing, etc.
With data complexity only increasing with every passing year, there is a pressing need for a centralized, flexible, and high-performance system that can handle diverse data requirements such as SQL analytics, real-time monitoring, data science, and machine learning.
What is a Lakehouse?
Advances in data analytics mechanisms and innovations in AI have led to a more complex & multiple data storage, analysis & governance systems. A standalone data warehouse is not optimized for such complexities, compelling data architects to rely on multiple data lakes, data warehouses, and other specialized systems to process modern-day data.
A lakehouse is a new approach to data storage and management that combines the best of both worlds – data lakes and data warehouses. It offers a new and open architecture enabled by better system design, by merging ease of access & support for enterprise analytics capabilities found in data warehouses with the relatively low cost & flexibility of data lakes.
What Value can Enterprises Derive from it?
Lakehouses eliminate the need for organizations to use a multitude of systems for data storage and management, thus removing unnecessary management overhead. For instance, the transactional metadata layer makes it possible for architects to apply data management techniques on top of raw, low-cost storage while constantly caching, indexing, and optimizing data.
They further allow organizations to leverage improvements in data architectures, data processing mechanisms, and metadata management to capture all data to a common platform and efficiently share it for machine learning and BI applications.
Using lakehouses, organizations can:
- Enable concurrent reading & writing of data across different data pipelines using SQL
- Help data architects use multiple BI & Reporting tools directly on source data, thus reducing latency, improving recency, and lowering costs
- Decouple storage from computation, to allow multiple concurrent users and support larger data sizes
- Ensure open and standardized storage formats, avoid vendor lock-in, and support a range of modern APIs
- Power diverse workloads, including data science, machine learning, and SQL and analytics
Key Challenge in Lakehouse Adoption
Although lakehouses can simplify the entire approach to modern-day data engineering by providing a centralized repository for all types of data and applications, they are not easy to implement. Being a relatively new kind of architecture, many organizations are still unaware of the technology or unsure of how best to harness the benefits it can provide.
Likely, organizations may need to upskill their current data engineering team or invest in an entirely new one with required skillsets associated with running complex metadata management layers, that eventually organize/optimize data on the fly. At the same time, businesses may also need to on board competent data architects & data scientists who can optimize data storage & representation in response to specific AI or ML workloads.
How can an Expert Partner Help?
Bringing in years of experience & expertise in complex data engineering & management, a partner like Ellicium can help build a robust lakehouse and ensure that data available to decision-makers is always complete & accurate and caters to all data types & formats.
By allowing storage of all structured and unstructured data at large scales in a lakehouse, Ellicium can enable complex processing and run different types of analytics for timely and accurate decision-making.
Ellicium can help:
- Provide advisory services & a roadmap on how to build a lakehouse
- Design a tailored lakehouse, keeping business goals and end-user reporting needs in mind
- Set up an efficient lakehouse using Big Data and cloud technologies like Hadoop, AWS, Azure, etc.
- Efficiently extract unstructured data from external sources and parse it using
- NLP techniques and machine learning algorithms
- Build the right frameworks for ingestion and analysis of real-time data from systems, IoT devices, and logs
- Enable effective data modeling based on industry standards and business-specific models and requirements
- Deploy ETL tools like Talend, Informatica, etc. as well as schedulers for integrating, orchestrating, and scheduling the lakehouse processes
- Implement required level of security and governance while also adhering to industry standards, best practices, and benchmarks
- Enable native support for AI and machine learning algorithms while optimizing data for fast query performance
By offering improved architecture, necessary data versioning, governance, and security controls, lakehouses radically simplify enterprise data management without having to toggle between multiple systems. They allow analysis of structured/unstructured data via modern AI and BI tools to keep pace with modern-day analytics requirements.