Legacy systems are failing you: Meet the modern data architecture

Data architecture has come a long way from its early days of relational databases and data warehousing. 

For decades, legacy data architecture has served us well. But with over 400 million terabytes of data created every day, it's no longer built for the scale or speed we need. To keep up, organizations are modernizing with cloud-native architecture. This gives them the speed, scale, and data quality to power informed decision-making. 

Modernizing data architecture is not a one-line solution. It requires exploration, examples, and implementation guides. In this post, we'll break down what modern data architecture actually looks like in practice: how to ingest, store, process, and analyze data in a way that supports your organization’s growth.

The core elements of modern data architecture

In your evaluation of modern data architecture, keep an eye out for these components:

Batch and real-time data ingestion

Modern data architecture is built to handle various data sources, including structured, unstructured, and streaming data. 

Batch ingestion sends data in chunks at regular intervals. It’s slower but easier on your computing resources. Tools like Apache Hadoop, Apache Spark, and AWS Glue move data in batches.

For real-time data, modern data platforms use Change Data Capture (CDC) to capture data as it arrives. Instead of reloading entire data, only the changes made to the source are updated in the destination to sync them.

Data storage 

At the heart of modern data storage are data lakes and lakehouses. Data lakes made storage much cheaper by storing data in its native format within object storage. 

Lakehouses take this a step further. They combine the best of both worlds, offering the centralized access and ACID properties of a data warehouse (the Atomicity, Consistency, Isolation, and Durability of its transactions) with the storage flexibility of a data lake.

Divide your data storage architecture into three zones for storing different types of data: 

• The Raw zone stores data in its native format as it’s ingested. This is intended for long-term storage. 

• The Transform zone holds processed data, and its lifetime depends on the specific use case of the business application — it's not meant for long-term storage. 

• The Curated zone contains refined, ready-to-use data, typically for analysis and reporting.

Data catalog 

There’s always a dataset you’re trying to find, a column’s data type you need to know, and context on what a specific table stores. A catalog layer provides that information for all your datasets spread across warehouses and lakes. 

Modern catalogs are built to automatically detect the storage layer schema and reflect it in the catalogs, keeping them in sync without manual effort. 

Build a discovery search tool on top of your catalog layer for end-user access. It's like having a powerful search engine just for your data. Search, see the metadata — columns, definitions, size — and decide whether the dataset fits your needs. Only then do you pull the data.

Data transformation & analytics

Data analytics is central to understanding what drives business, sales, and profits. But to make it work, you need clean and organized data. 

Data transformation is how you get that — cleaning the data, combining it, standardizing it, and turning it into a structured format that’s ready for analysis.

Here’s a quick look at the tech stack that powers modeling and transformation for different warehouse choices:

• SQL-based tools like dbt are perfect for data modeling and transformation in the warehouse (e.g., Snowflake, Bigquery). 

In the lakehouse architecture (e.g., Databricks, Delta Lake, Apache Iceberg), you’d use Spark, SQL, or Python for transformation. Modern data architecture also supports real-time data transformation. Use stream processing engines like Apache Flink, Apache Spark Structured Streaming, or Materialize to transform data as it arrives.

Security & Governance 

Modern architecture puts data security and governance front and center. Data governance policies cover everything from who gets access to what to how you tag and categorize sensitive data. It allows you to stay compliant without putting sensitive or customer data at risk.

Key steps for implementing a modern data architecture

This is the how-to part. These are the practical, actionable steps you can take to build a modern architecture that scales with your data.

Establish a unified data storage system

When you have structured data, cloud data warehouses like Snowflake, BigQuery, and Redshift are essential to modern data stacks. They are great for simplicity, faster queries, and analytical reporting.

But when your data is unstructured, start by dumping it into a lake. No cleaning. No reshaping. Just store it as-is. Then, add a lakehouse layer (also known as an abstraction layer) using table formats like Delta Lake, Apache Iceberg, or Hudi.

While the data sits in its native format in the lake, the table format layer on top adds structure, schema management, versioning, and ACID guarantees. So, it makes your big data shine like SQL tables.

Integrate real-time event pipelines

The combo of Apache Kafka and Apache Flink is a game-changer in real-time event processing. Both are open-source. 

Kafka acts as a high-throughput messaging system, collecting and storing events in Kafka topics. Flink then consumes those events, processes them in real time, and stores them in your lakehouse. It’s a powerful combination — if you’re into building from scratch.

But, if that’s not your thing, there are managed services. Amazon Kinesis or Azure Stream Analytics are easier to implement (but come with a price tag). 

Enforce federated governance

As you establish your data mesh architecture, you’ll want to keep its structure and data well-protected. One way to do this is through federated governance.

Here's how it works: a central team defines the high-level governance rules. They’ll say things like, “Only approved users can access sensitive data,” or, “Data access requires approval.”

Then, domain teams take it from there. They implement those rules in ways that make sense for their data. For example, they might set role-based permissions so the marketing team can’t access financial data unless they submit a request and get it approved.

You don’t have to implement this from scratch, either — there are tools for it.

Data governance tools come with built-in frameworks that follow industry best practices. You can customize them to fit your org’s data needs. Set access controls, define quality thresholds, and enable auditing — all from one place.

Adopt a cloud-native approach

Cloud architecture separates storage and compute components, so you can scale each one independently and only pay for what you use.

Faster performance, more storage, plus elasticity and scalability, all at a lower cost. These are all reasons to break down legacy systems into cloud-native data workflows.

For implementation, pick a cloud provider like AWS, GCP, or Azure. They all come with object storage built in. Think S3 on AWS — that’s your data lake. Then use services like EC2 or Google Compute Engine to handle computations.

In contrast, if you’re building your own cloud-based architecture, rely on principles like microservices, containerization, and elasticity.  

Incorporate AI horsepower 

You can further streamline all your data architecture layers with AI. For data quality, AI-powered tools can automatically detect inconsistencies, errors, and outliers using machine learning algorithms.

In data governance, modern platforms use artificial intelligence to tag and classify sensitive data, making compliance easier and more reliable.

Analytics is getting simpler, too. For instance, Hex Magic lets your stakeholders perform self-service analytics through natural language prompts. By understanding your warehouse schemas and semantic models, it can write accurate queries, auto-complete joins, and even handle tricky date functions.

Bring CI/CD to your data products 

If DevOps delivers software products at scale and speed, DataOps brings the same magic to data products. It automates testing and deployments using version control and CI/CD principles.

This helps orchestrate your ETL/ELT data pipelines and ensures they don’t suddenly break when everything is deployed at the end. Not sure where to begin? Here’s how we built CI/CD into our data warehouse. 

When teams treat data as a product, self-serve platforms become more successful. Anyone with the right permissions can find and use them — without filing a ticket or learning SQL magic.

It’s a far cry from the chaos of data silos, where one team hoards the data and no one else can touch it. Data as a product breaks down those walls.

Advanced analytics

Modern cloud data architecture plays nicely with analytics. From ingestion to storage to processing, it’s all designed to serve the end use case: advanced analytics.

That’s why cloud platforms in the modern stack integrate easily with analytics and business intelligence tools. 

Take Hex, for example. It’s the only unified, AI-powered platform for data analytics. Its native integrations with Snowflake, BigQuery, and Redshift mean data scientists can pull in data from those warehouses, run analysis, and build models and dashboards — right inside Hex notebooks.

Common roadblocks in legacy data systems

Growing data volumes are a major problem. Legacy systems simply can’t handle it. They’re stuck with storage and hardware limitations, whether it’s on-prem infrastructure or the high costs of centralized storage.

And because these systems don’t separate storage and compute, companies end up paying more, regardless of how much they actually use or process the data.

Legacy data systems also struggle with modern data types. They’re built for structured data, sure, but fall short when it comes to streaming data processing. With the increasing demand for real-time analytics, companies need to rethink their data architecture to stay competitive.\

According to a Forbes Council post, many businesses still struggle to tap into the full potential of big data because they lack modern analytics tools. Traditional data architecture makes things worse, as it often fails to connect well with modern tools. Without that integration, businesses continue to fall behind, unable to fully capitalize on analytics tools and unlock the value of big data. Modern data architecture aims to seamlessly integrate between tools, simplify data management, and empower business users to perform their tasks without relying on IT. 

Building the business case for architectural modernization

Rewriting your legacy data systems in a new tech solution isn’t modern data architecture. It’s more than that. Modernization is about solving deeper problems, like tight coupling and poor scalability. It means: 

• Rethinking everything with the end use case in mind: For many orgs, the end use case of data is decisions based on analytics that lead to business growth. So, we need to design an architecture that empowers us to make use of data more effectively. 

• Tracking the business side of things: Start by calculating the ROI (Return on Investment) by asking your stakeholders and TCO (Total Cost of Ownership). This gives you a clear picture of the value modernization can bring.

• Staying synced with clear goals and KPI dashboards: Without them, it’s tough to know if you're optimizing for the right things — whether you’re trying to cut costs, scale efficiently, or improve data usability (or maybe all of the above).

So, how do you approach modernization? A phased implementation helps. You can begin by assessing how you store data — does a data mesh, data lake, or lakehouse serve your needs best? 

Then, move into modernizing other pieces: transformations, integrations, analytics, and others.

Modernize data analytics workflows

You’ve modernized your architecture — your data is clean, accessible, and ready to use.

Now, it’s time to modernize your analytics workflows.

Your data team works across SQL, Python, and R to turn data into decisions. They can do this all in one place without switching tools, thanks to Hex’s multi-modal workflows. Grab the guide to bring multi-modal workflows to your team.