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By Charlie Flowers, Senior Data Engineer, Data Surge

As the Information Age accelerates ceaselessly, data grows in volume and is just as quickly rendered obsolete by newer data. Racing against this avalanche of information, an organization’s ability to process and gain actionable insights is paramount. Traditional ETL/ELT and data warehousing systems struggle to match the pace but are expensive and risky to replace in a mission-critical environment.

As more and more technological band-aids are applied, these environments become brittle, and responsibilities become siloed; it becomes increasingly difficult for the developers of a data platform to anticipate and respond to the evolving needs of those consuming its data. These deficiencies in data, analytics, and governance are frighteningly common and can affect data operations across the entire enterprise, creating an environment where users cannot easily create or retrieve reports, data models are developed without the input of SMEs, and with no clear chain of custody to ensure that data is accurate and up to date.

Bottom line: the right insights don’t get into the hands of the right people at the right time.

In this blog post you’ll learn how to avoid these pitfalls and unleash the full capability of your organization’s data by making improvements in people, processes, and technology. You’ll also learn about the six benefits of utilizing a Data Lakehouse architecture, and explore how to implement trustworthy ways of ingesting, storing, processing, monitoring, analyzing, and sharing your data at speed and scale.

A Data Lakehouse is a modern data architecture championed by the data and AI company Databricks. It allows users to combine the key benefits of data lakes, which are large repositories of raw data, and data warehouses, which are organized sets of structured data. Taking the best from both worlds, this type of architecture helps reduce costs and generally produces faster, more accurate results.

Here are six key benefits of a data Lakehouse approach:

1: Data Centralization

Different teams in an organization may make use of the same data, but for very different purposes and with different rules and standards, which can easily lead to the problem of Data Silos. Lack of standardization makes cross-enterprise analytics hard and creates roadblocks for proper data governance.​ The first step in creating a Data Lakehouse is to centralize data and provide access to everyone across the enterprise based on ‘least-privilege’ principles.

[Read our article: Azure Implementation of the Data Lakehouse]

With the Data Lakehouse architecture, raw data is centralized, and then cleansed, enriched, and standardized. This process is instrumental to the success of any project. For anyone who has run an AI/ML project or two, you know how imperative it is to get clean data, because it’s Garbage-In-Garbage-Out, and if you really want your advanced ML models to perform with accuracy, this is a critical stage in the Lakehouse approach.

A Data Lakehouse architecture also helps support integrations for real-time streaming datasets. And when it comes to ML models, training data is one of the key components. Data centralization can really help provide augmented training datasets.

Next, the data is ready to be given “decentralized” access for general use to the stakeholders across different teams within the enterprise.

2: Data Governance

People are an essential piece of the data governance puzzle. They have an important role to play to ensure that an organization’s data is accessible, usable, safe, and trusted. It is critical that every aspect of the data lifecycle (i.e. from creation, preparation, usage, storage, and archiving to deletion of the data) must be established using data governance principles that the organization adheres to. The Data Lakehouse approach helps ensure that data quality and integrity stay intact throughout the data lifecycle.

Also, as you develop your AI strategy, ML-Ops becomes another critical component of your journey and will allow you to adhere to model governance best practices. After all, data scientists need the ability to track experiments, measure and compare evaluation metrics, and manage model promotion and deployments.

With data centralization and governance in place, you will take your first step towards a data-driven culture and help your organization make the most of its data for a competitive and strategic advantage. But you’re not done yet.

3: Data Quality Gates and Data Monitoring

The ability to ensure that any data that’s being processed continues to adhere to quality standards is key to maintaining data integrity. Many times, either the source data format has changed, the meaning of the data has changed, or perhaps a new piece of code was introduced which impacted the data’s fidelity. Each of these scenarios can be handled through quality controls that can ensure that data and business rules continue to stay logical.

Creating data quality ‘gates’ to continuously monitor data is an approach that will provide you with extra peace of mind, knowing that your organization can continue to put their trust into the data they are working with. Then, if there is an issue with the data, someone in the organization will be alerted and bad data won’t get into stakeholder’s hands. This is a huge milestone for any data-driven organization.

Following these three steps will give your organization the ability to be more agile in updating its data pipeline, and to add new data products, reports, insights, and visualizations on a weekly basis. Now, we are talking speed!

4: Data Security and Data Sharing

In today’s world, data security should be on top of everyone’s mind. It’s critical to implement policy-governed controls that enable data stakeholders to process and query data in a secure way. Various datasets should be protected to ensure proper access and auditability. Data governance, combined with data security, allows organizations to rapidly scale – from a single use case to operationalizing data and AI platforms across many distributed teams.

5: Data Orchestration

As your data Lakehouse grows in scale and complexity, it can become difficult to manage your ETL processes. The solution is to employ a data orchestration tool to schedule your workflows, handle error conditions, and provide insight into the current state of your Lakehouse. Databricks has a built-in ‘Workflows’ tool which is relatively full featured, but for more advanced orchestration tasks you might want a third-party orchestration tool.

6: Data Visualization

Once you have taken all the steps above, data visualization is where data truly comes to life. Again, Databricks easily integrates with tools like Tableau, and Power BI. It also comes with its own internal tool for quick and easy visualizations.


Over the past several years, the Data Lakehouse model has grown to be the consensus choice for building a scalable data platform that iterates on and addresses the shortcomings of its predecessors in the data warehousing/data lake space. From humble beginnings, it has grown into a holistic suite of tools that encompass virtually every need for a modern data stack. Today’s data ecosystem – with the Lakehouse at its center – empowers us as developers to unleash data at virtually any scale in a matter of minutes.

At Data Surge, we are proud to partner with cutting-edge technology companies like Databricks and others to provide our clients with the industry-leading tools, processes, and best-practices that address their data challenges. If you’re interested in learning how to improve the quality and usability of your organization’s data, contact us today.

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