Big 3 Square-Off: Data Warehouses vs. Data Lakes vs. Feature Stores + Molecula’s Enterprise Feature Store

By: Laura Komkov

Big 3 Square-Off: Data Warehouses vs. Data Lakes vs. Feature Stores + Molecula’s Enterprise Feature Store

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When it comes to managing, processing, and storing the gargantuan volumes of data that are constantly created in today’s world, the options can seem endless. We’re going to walk you through a couple of those options along with the pros and cons of each.

*Data Warehouses

Molecula Data Warehouse

The data warehouse created access to organized data within enterprise organizations by centralizing data within a single platform in an archived, structured way.  Data warehouses process and transform data for analytics in a structured database environment where data can be queried to help make business decisions. They can often be seamlessly integrated with visualization tools like Tableau and Power BI to derive insights. 

The Data Warehouse allows for historical insights, enabling businesses to look back at data and to react, but the data warehouse does not allow for predictive activity due to its performance restraints. Most data warehouses were designed taking into consideration the requirements data scientists have when performing business intelligence initiatives, not advanced analytics, and that’s why many organizations struggle to implement machine learning and artificial intelligence solutions with data warehouses today.


Data Lakes

Molecula Data Lake

Data lakes are similar to data warehouses in that they are both data storage structures, but in a data lake, there is no hierarchy or structure to your data. The data lake can ingest data from disparate sources, and holds data in its native format – no matter the source or type, including structured, semi-structured and unstructured data – until it’s ready for use. Data lakes were built around the premise of being able to aggregate your data into one central location to avoid data silos. 

A data lake is an effective solution for companies that need to collect and store a lot of data, but do not need to process and analyze it right away. Because data lakes do not care about the format data is in, it makes them a great tool for aggregating data. However, that also means that data lakes are filled with many different data types and a lot of data, which results in poor direct query performance compared to other solutions. When it comes to creating measurable value, another analytics infrastructure tool or a performant layer on top of a data lake is almost always needed. Additionally, traditional data lakes often lack data governance and security controls.

To work around data lake limitations, users often end up extracting subsets of data  from the data lake and replicating those subsets within a data warehouse. This process typically requires IT assistance, slows down the time to insight, adds costs, and, in the end, undermines the benefit the data lake was intended to bring to the business.


Feature Stores

Feature stores are a necessary component within the operational machine learning stack. They are essentially a “data store” for machines, holding compute-ready data in a central repository so that it can be used by different teams throughout an organization to power model training and deployment. Data warehouses and data lakes are two of the inputs that can provide data to the feature store, along with streaming data, and additional data sources.

With most feature stores, data must be transformed, aggregated, and validated before it is ingested into the feature store. Feature pipelines are written to ensure that data flows reliably into the feature store in a format that is ready to be consumed by machine learning training pipelines and models. The feature store serves feature vectors for training and production purposes and allows for the re-use and sharing of features inside and outside of an organization.

Most current feature stores focus heavily on operational machine learning, and are built on reference architectures that use row-oriented and columnar formats.


Molecula’s Feature Store

Molecula’s feature store is different. Other “feature stores” do not solve the problem of real-time, compute-ready data at scale. Instead, they employ reference architectures to create an additional data store that feeds directly into model training and deployment workflows. Because they are dependent on existing technologies and data formats, they cannot scale (performance or storage-wise) to meet the demands of machine-scale analytics and AI, even with numerous optimizations in place. 

Molecula, on the other hand, makes  your most important data instantly computable, and creates a true feature store — a database for compute-ready features. We leave data at its source and continuously extract and update only features (in a highly compressed, highly performant, feature-first data format) into a centralized feature store. This process eliminates the need to copy, move, or pre-aggregate data, reduces the data footprint by 60-90 percent, and provides a secure data format for sharing. All of an organization’s data can be converted to reusable features and analyzed with full fidelity, regardless of format or source location, across any cloud, for immediate, millisecond analytics performance.

Molecula’s feature store is not built on any existing architectures — it is an entirely original technology, based on our data format, that can scale without sacrificing speed or latency. It does not create another silo, but instead eliminates existing silos, unifying access to all data for all teams. With Molecula, you will no longer need to use a data warehouse (unless you want to – that’s your call, not ours!).

Molecula allows you to extrapolate the trends, patterns and insights held within your entire data set and quickly test those trends and patterns via models, so that your business can iterate rapidly in a truly predictive way.

* please note, we classify Data Warehouses as cloud-based data warehouses for the purposes of this blog — if you have questions about on-prem vs. cloud and how they’d interact with Molecula, please contact us!


How do Data Lakes, Data Warehouses and Molecula’s feature store compare?

 

Data

Data warehouse Data lake Molecula’s feature store
Holds only data that is structured/organized and is necessary to business problems. Holds all data – structured, semi-structured, raw, regardless of whether it is necessary or not. Holds only the features (i.e. attributes) extracted from data, allowing for much faster query speed and eliminating security/compliance risks.

 

Agility

Data warehouse Data lake Molecula’s feature store
Highly cumbersome and time-consuming to make any changes to the structure of a data warehouse. Lacks structure, and therefore relatively easy to make changes. Can be configured and reconfigured as necessary. Extremely simple to make changes – real-time updates flow freely.

 

Users

Data warehouse Data lake Molecula’s feature store
Specific business users who need to report on or extract particular meanings (with use cases defined during setup of data warehouse) Data scientists are often the end user because of the skills needed to approach unstructured data for deep analysis. Data engineers, data scientists, app developers, and any other teams/users within an organization who are in need of predictive and prescriptive business outcomes.

 

Security

Data warehouse Data lake Molecula’s feature store
Relatively secure when implemented properly as data is structured and access is limited. Can present security concerns since all data is stored in one unstructured repository, potentially making data more vulnerable. Eliminates security and compliance risks as no raw data is actually stored within the feature store — only the features/attributes. 

 

ML/AI-Readiness

Data warehouse Data lake Molecula’s feature store
Data is not in a compute-ready format Data is not in a compute-ready format Compute-ready data format can be used for model training and deployment

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