Ensure your CEO isn’t making decisions based on outdated data. At dbSeer we empower companies like Authority Brands to transition from slower, manual reporting to real-time analytics, eliminating hours of monthly reporting and transforming KPIs into actionable insights. The key, as we see it? A modern data lake that delivers timely, useful information for business leaders.
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To understand how this transformation happens, let’s clarify what defines a modern data lake.
Think of a modern data lake like your phone’s photo gallery. Everything is accepted as-is—raw data from various sources, mixed together in their original format. For example, structured CRM data is often paired with unstructured social media data. IoT devices are alongside financial reports from relational databases. All data is stored in a centralized repository that handles large amounts and diverse types of data.
Unlike traditional data warehouses, which require you to process data upfront using predefined schemas, data lakes employ schema-on-read approaches. This means you can store data first, then determine data structures when analysis begins. This flexibility enables the development of advanced analytics and machine learning models that drive real-world business value.
When dbSeer helped Abt Associates democratize data access for over 2,600 global employees, we developed a cloud data lake approach that transformed monthly sales updates into twice-daily refreshes while integrating more than 10 data sources in just months.
The dbSeer Assessment-First Process for Data Engineering
Most companies fail to build modern data lake architectures because they start backwards—buying cloud platforms and then figuring out their business needs. Our data engineering process ensures you build exactly what your organization’s data strategy requires:
We begin by evaluating your current data systems. What decisions need better data access? Where are insights trapped in data silos? We identify sensitive data, data volumes, and existing data infrastructure before touching any technology.
Based on the initial assessment, we create a scalable cloud-native architecture (designing platforms using internet-based infrastructure). This includes designing the ingestion layer (where data first enters the system) for streaming data (real-time data as it is created) and batch processing (handling data in groups). Then the storage layer (where data is kept long-term) is used, either Amazon S3 or Azure Data Lake Storage. Next, the processing layer (where data is transformed and analyzed) with Apache Spark (a large-scale data engine) or other platforms. And then, finally, the consumption layer (where users access results) for end users.
As an AWS Advanced Partner, we build data pipelines (automated paths that move and transform data) using proven AWS services that efficiently connect diverse data sources, from semi-structured data feeds to traditional databases. Our data engineers implement AWS Glue (a cloud-based data integration tool) for data transformation while maintaining data lineage (tracking data origins and changes) throughout the data flow.
This prevents the dreaded data swamp (a disorganized, unusable data storage system). We implement data catalog systems (tools for organizing and indexing data), access controls for sensitive information, and quality checks. Proper data discovery tools (to help users find relevant information) ensure business users and data scientists can easily access the data they need.
Data Lake vs. Data Warehouse: Implementing Both
Smart businesses implement data lake and data warehouse solutions that leverage the strengths of each system to optimize their data management. Data lakes excel at storing large amounts of data from diverse sources for data science and exploratory analytics. Traditional data warehouses provide reliable data for standard SQL queries and business intelligence.
dbSeer helped Authority Brands implement this hybrid approach—we architected their Amazon Redshift data warehouse to process 1,000+ franchisee transactions while building their data lake to enable advanced analytics across diverse data types. This medallion architecture (layered approach for data organization) ensures both real-time analytics and structured reporting capabilities.
Getting Started: Assessment Before Architecture
Don’t start with technology. Start with business needs: What decisions require better data access? Where are valuable insights currently trapped? Who needs access to what common data, and how do they prefer to work with it?
Our most successful clients with cloud data lakes didn’t overhaul everything at once. Instead, they started with careful assessments, then built strategically, and scaled as needs evolved. To gain a competitive edge with your data, start by understanding what your business truly needs from its data infrastructure—not simply by acquiring new technology. Transforming your scattered data systems into a real advantage starts with a clear, focused strategy. Reach out to dbSeer today!
