The modern dataset represents the evolution of data management, moving from traditional, monolithic systems to agile, cloud-based architectures. It is designed to handle large amounts of data, providing scalability, flexibility and real-time processing capabilities. This suite is modular and enables organizations to use specialized tools for each function: data ingestion, storage, transformation and analysis, facilitating a more efficient and democratized approach to data analysis and business operations. As companies continue to prioritize data-driven decision making, the modern dataset has become integral to unlocking actionable insights and driving innovation.
The evolution of the modern dataset
Early days: before the 2000s
Companies use large unified systems to store and manage their data. They were good for day-to-day business tasks, but not so much for analyzing piles of data. Data is stored in traditional relational databases such as Oracle, IBM DB2 and Microsoft SQL Server.
The Big Data Era: Early 2000s – 2010s
This period marked the beginning of a shift towards systems that can handle large amounts of data at high speeds and in a variety of formats. We started seeing a lot more data from all over and it was coming in fast. New technology like Hadoop helped by spreading the work with data to many computers.
The rise of cloud data storage: mid-2010s
Cloud computing has begun to revolutionize data storage and processing. Cloud data warehouses such as Amazon Redshift and Google BigQuery have offered scalability and flexibility, changing the economics and speed of data analytics. Also, Snowflake, a cloud-based data storage startup, has emerged, offering a unique architecture that separates compute and storage.
Modern data set: Late 2010 – present
The modern dataset has taken shape with the rise of ELT processes, SaaS-based data integration tools, and the separation of storage and compute. This era saw the proliferation of tools designed for specific parts of the data lifecycle, enabling a more modular and efficient approach to data management.
Limitations of traditional data systems
In my career as a data engineer, in several organizations, I have worked extensively with Microsoft SQL Server. This section will draw on those experiences, providing a personal touch as I recount the challenges facing this traditional system. Later we will explore how the Modern Data Stack (MDS) solves many of these problems; some solutions were a real revelation to me!
Scalability
Traditional SQL Server deployments were often hosted on-premises, which meant that scaling to accommodate growing amounts of data required significant hardware investment and could lead to extended downtime during upgrades. Moreover, when we had less data to work with, we still had all this extra hardware that we didn’t really need. But we paid them anyway. It was like paying for a whole bus when you only need a few seats.
Complex ETL
SSIS has been widely used for ETL; although a powerful tool, it had certain limitations, especially when compared to more modern data integration solutions. Notably, Microsoft SQL Server has addressed many of these limitations in Azure Data Factory and SQL Server Data Tools (SSDT).
- API calls: SSIS initially did not have direct support for API calls. Interacting with web services required custom scripting, which complicated ETL processes.
- Memory allocation: SSIS jobs require careful memory management. Without enough server memory, complex data jobs could fail.
- Revision: Extensive auditing within the SSIS package was necessary to track and resolve issues, which added to the workload.
- Version control: Early versions of SSIS presented challenges with version control integration, complicating change tracking and team collaboration.
- Accessibility on different platforms: Managing SSIS from non-Windows systems was difficult because it was a Windows-centric tool.
Maintenance requirements
Maintaining local servers required a lot of resources. I remember the considerable effort it took to ensure systems were up to date and running smoothly, which often involved downtime that had to be carefully managed.
Integration
Integrating SQL Server with newer tools and platforms has not always been easy. This sometimes required creative solutions, which further complicated our data architecture.
How a modern dataset solved my data challenges
Modern Data Set (MDS) fixed many of the old problems I had with SQL Server. Now we can use the cloud to store data, which means no more spending on large, expensive servers that we may not always need. Getting data from different places is easier because there are tools that do it all for us and there is no more tricky coding.
When it comes to sorting and cleaning our data, we can do it directly in the database using simple commands. This avoids the headaches of managing large servers or digging through tons of data to find a tiny bug. And when it comes to keeping our data safe and organized, MDS has tools that make it extremely easy and less demanding.
So, with MDS we save time, we can move faster and there is much less hassle. It’s like having a bunch of smart helpers taking care of the hard stuff so we can focus on the cool part — finding out what the data is telling us.
Components of a modern dataset
MDS consists of different layers, each with specialized tools that work together to simplify data processes.
Data entry and integration
Extract and load data from a variety of sources, including APIs, databases, and SaaS applications.
Swallowing tools
pettran, stitch, airbyte, segment, etc.
Data storage
Modern cloud data warehouses and data lakes offer scalable, flexible and cost-effective storage solutions.
Data warehouses in the cloud
Google BigQuery, Snowflake, Redshift, etc.
Data transformation
Tools like dbt (data builder tool) enable transformation within the data warehouse using simple SQL, improving traditional ETL processes.
Data analysis and business intelligence
Analytics and business intelligence tools enable advanced data exploration, visualization and insight sharing across the organization.
Business intelligence tools
Tableau, Looker, Power BI, Good data
Data extraction and reverse ETL
It enables organizations to operationalize their warehouse data by moving it back into business applications, driving action from insights.
Reverse ETL tools
Hightouch, census
Data orchestration
Platforms that help automate and manage data workflows, ensuring the right data is processed at the right time.
Orchestration tools
Airflow, Astronomer, Dagster, AWS Step functions
Data management and security
Data governance focuses on the importance of managing data access, ensuring compliance and protecting data within MDS. Data Governance also provides end-to-end management of data access, quality and compliance, while offering an organized inventory of data assets that increases visibility and reliability.
Data catalog tools
Alation (for data cataloging), Collibra (for management and cataloging), Apache Atlas.
Data quality
Ensures data reliability and accuracy through validation and cleansing, providing confidence in data-driven decision making.
Data quality tools: Talend, Monte Carlo, Soda, Anomolo, Great Expectations
Data modeling
It helps design and easily iterate database schemas, supporting agile and responsive data architecture practices.
Modeling tools
Erwin, SQLDBM
Conclusion: cost-conscious adoption of MDS
The modern data set is quite amazing; it’s like having a swiss army knife for handling data. It definitely makes things faster and less of a headache. But while it’s super powerful and gives us a lot of cool tools, it’s also important to keep an eye on the price. Pay-as-you-go cloud pricing is great because we only pay for what we use. But, just like a phone bill, if we’re not careful, those little things can add up. So while we enjoy the great features of MDS, we should make sure we stay smart in how we use them. That way, we can continue to save time without any surprises when it comes to costs.