In today’s data-driven world, whether you are a software developer, data scientist, or CEO of a mega-tech company, working with data is likely an integral part of your daily routine. Data is undeniably a crucial part of any business's success, yet to extract valuable insights from such an influx of information requires an efficient, reliable, and highly adaptable data pipeline that can process, analyze, secure, and store these data assets amid the rapidly evolving tech landscape—entering the modern data stack.    

Compared to traditional data processing mechanisms that often involve cumbersome and rigid processes that are not only costly to build but also difficult to scale, a modern data stack offers significant advantages through its cloud-based infrastructure. Since the stack consists of distinct, interchangeable components, this sense of modularity allows companies to add, remove, or replace data components as needed, making it highly adaptable to new changes. Such flexibility supports greater scalability and maintainability, enabling organizations to effectively handle growing data needs and efficiently integrate new tools with enhanced capabilities. 

In this blog, we walk you through the essential steps to building a scalable and agile modern data stack that excels in real-time decision-making, offering insights on how to build a data stack specifically customized to meet your organization's evolving demands throughout every step of your business development.

What is a modern data stack?

According to Dataversity, a modern data stack is simply a collection of tools used to “collect, store, and analyze data.” The tools and technologies in a modern data stack are designed to process large volumes of data and support real-time analytics so that organizations can derive actionable insights quickly and make data-driven decisions. Ultimately, the goal of a modern data stack, compared to a relational data system, is to meet the demands of a complex data infrastructure with maximum efficiency.  

Why adopt a modern data stack?

Modern data stack offers so much more than just traditional data management—it enables data engineers to build scalable and robust data pipelines, data analysts to explore and transform data for insights, and decision-makers to access and visualize data for market analysis. By offering a comprehensive suite of tools for data ingestion, processing, and visualization, the modern data stack facilitates seamless integration, real-time analytics, and greater flexibility, ultimately enhancing companies’ ability to leverage data as a strategic asset. 

1. Versatile Scalability and Flexibility 

Modern stack stacks allow organizations to scale computing resources up or down on demand, adding, removing, or replacing tools and services at different stages of their business development. 

2. Improved Efficiency  

Since modern data stacks run on a cloud-based system, companies can automate repetitive tasks and workflows that streamline the data processing and transformation process, allowing for faster analytics and enhanced self-service data analysis capabilities. 

3. Enhanced Cost-Effectiveness 

When it comes to budgeting, adopting a cloud-based system often means that companies can pay-as-you-go, saving money on infrastructures that they don’t currently need or desire. This way, the maintenance and operational costs are significantly reduced due to optimized resource allocation. 

4. Secured Data Transition 

Despite having distinct functionalities, most data tools come with built-in features that simplify data governance and security management. Since these tools have to work together and share data, they follow rigid compliance with industry regulations and standards. This ensures robust access control and well-rounded protection of enterprise data assets, ultimately enhancing the company’s data integrity and overall security.  

5. Advanced Analytics and AI Tools 

In order to keep up with the evolving data landscape, modern data stacks are designed to support advanced analytics and machine learning projects. They are built with the flexibility to adapt to diverse data sources and types so that new technologies can be incorporated smoothly into any existing data flow. 

How to choose the right data tools?

To save you from sifting through countless data tools to find the perfect fit, we’ve compiled a list of data tools that excel in different aspects of data management and analytics, complete with practical use cases and recommendations. Consider industry-specific use cases and how others in your field are leveraging these tools when choosing the right data tools most appropriate for your organization. Think about the end users and how these data analytics can be applied across various roles within your organization.

Evaluate Key Components

Data Ingestion 

The process of data ingestion involves gathering data from various sources and bringing them into a centralized database. When choosing the right components for data ingestion, consider both the diversity and the volume of data you’re looking to process on a regular basis and how fast you’d like them to be processed. 

Take the healthcare industry as an example: medical data often comes from disparate sources and forms such as written surveys, graphs, and medical reports. In the beginning of 2024, the Public Policy Forum published a report specifically calling for the urgent modernization of the data infrastructure underpinning the Canadian healthcare system. Due to the fragmented nature of these data sources, the data ingestion tool that healthcare companies should be looking for should have high processing capability and flexibility for diverse data types integration. 

Here are some of our recommendations: 

Fivetran excels in its straightforward setup and ease-of-use interface. It automates the ELT process for data processing, saving significant DevOps time and resources. 

Airbyte boasts over 350 connectors–such an extensive library allows businesses to integrate a variety of data sources and destinations, including popular platforms like MySQL, Salesforce, and Big Query. As an open-source platform, it also significantly reduces the cost of ownership compared to other closed-source alternatives. 

Apache Kafka offers several key advantages, including its horizontal scaling capabilities and high throughput. It also has a rich ecosystem of tools and frameworks, such as Kafka Connect that integrate well with the platform, making it a powerful tool for building real-time data pipelines and streaming applications across various industries.

Data Processing and Visualization 

Once data has been fed into the system, processing tools clean and organize it, preparing it for analysis and insight extraction. When choosing the right tool to transform raw and fragmented data into categorized, informative pieces and ultimately actionable business insights, consider both the type of analysis you need (e.g., description, prescriptive, predictive) and the demographics of the users. 

Take the retail industry for example, stronger cloud computing capabilities enable retailers to access and process large volumes of data from various sources without investing in expensive infrastructure. The ability of the data tools to process real-time data quickly and consistently also allows retailers to adapt quickly to customer behavior, demand, and feedback, enhancing inventory management, pricing strategies, and product personalization. 

Here are some of our recommendations: 

Amazon QuickSight is a cloud-powered BI platform that enables companies to create interactive visualizations, reports, and dashboards from a variety of data sources. QuickSight offers an intuitive and user-friendly interface that empowers both tech and non-tech users to gain valuable insights from the diverse datasets they work with. 

Apache Superset is an open-source tool that requires no licensing costs. It is designed to be accessible for non-programmers, with a no-code visualization builder, allowing users to build dynamic, interactive visualizations custom to business purposes.   

Microsoft Power BI excels in its ability to access image recognition and text analytics, and build machine learning models. It also allows real-time updates when new data is streamed or pushed into the dashboard. 

Data Storage

Many cloud solutions, such as Snowflake, AWS Redshift, and Databricks offer data warehouses, data lakes, and lake houses that help businesses store and process data. When choosing the appropriate data storage system, consider the volume requirements for performance and scalability--are you looking to store structured, semi-structured, unstructured data, or a combination of them? Additionally, make sure you’re in compliance with relevant regulations. You can also assess encryption, access controls, and various authentication features of your data storage system to ensure security. 

Take the financial industry as an example: strict data security and compliance are applied to the data circulated in the financial industry. They also demand high-speed transaction processing and long-term data retention for auditing, so the ability to scale up the storage and respond to disaster recovery are among the factors to be considered when selecting tools for data storage. 

A few popular tools to consider include: 

Snowflake excels in its highly-secured data storage. Users can set regions for data storage and adjust the security levels per request. The solution also has built-in features to encrypt all data at rest and in transit.  Services like Time Travel can be enabled to restore tables, schemas, and databases from a specific time point in the past.

Databricks leverages Delta Lake, an open-source storage layer that is easily scalable. It integrates seamlessly with major cloud providers' storage solutions such as AWS S3, Azure Blob Storage, and Google Cloud Storage and allows organizations to store and process data in their existing cloud storage infrastructure.

MinIO is a high-performance, S3-compatible object store that is built for AI/ML, advanced analytics, databases, data lakes and HDFS replacement workloads. It offers a rich suite of enterprise features targeting security, resiliency, data protection and scalability. 

Data Quality Control  

Data quality assurance tools are essential for maintaining accurate and reliable data across an organization. 

Taking organizations in the education system as an example, data quality is crucial to ensuring the accuracy of academic records and identifying students who may need additional support or interventions, allowing for timely and targeted assistance to improve their academic performance and overall experience.  

Here are a couple of popular data quality tools for you to consider:

Great Expectations is a data quality tool that enables companies to ensure the integrity and reliability of their data assets. Users can define expectations for their data while the system automatically checks if these standards are met. The user-friendly interface also allows the infrastructure to be integrated with various data storage and processing technologies. 

IBM Data Quality Info Server also provides end-to-end data quality management, including data cleansing and standardization capabilities to uncover inconsistencies and identify data patterns as well as anomalies. It also supports data governance by enforcing data quality regulations and policies, ensuring compliance with regulatory requirements and international standards.

Ready to Adapt

The tech industry is constantly evolving, which means that most of the tools and technologies available today will continue to advance and change. Remember that having a data stack is just a starting point–choose modular components that allow you to evolve your stack as your company grows and data needs change. By following a structured approach and understanding your data demands at all times, you can select the right combination of data tools to build an effective and scalable data stack for your organization.

Challenges that need to be addressed

As much as modern data stacks are built with the intention of assembling a team of “Avengers” of the data world, they don’t exist without limitations. In fact, creating such a powerhouse is confronted with many challenges, including the complexity of stack integrations, costs of deployment, and continuous security measures, especially as demand for high-quality data continues to grow. 

Complexity of Integration

As if the number of data tools and technologies available in the market today isn’t overwhelming enough, statistics show that only 28% of applications are properly integrated within organizations’ internal workflows. Choosing inappropriate data tools will not only lead to poor data quality and security risks but also result in inefficiencies and increased resources due to either a lack of or overlapping functionality.

Deployment and Maintenance Costs

Although implementing effective data tools can help companies save money in the long run, outsourcing data components involves managing multiple vendors, each with its own integration requirements and timelines. Additionally, deploying and maintaining these tools necessitates substantial expertise and in-house DevOps resources, which can be challenging for small-scale businesses to secure.

Data Security 

Managing a diverse array of tools presents considerable administrative and security challenges. Enterprises frequently handle thousands of interconnected pipelines across multiple clouds, each with distinct functionalities and security models. This complexity means that a single misstep or update can have far-reaching consequences, potentially impacting hundreds of pipelines.

Shakudo’s Role 

Shakudo is dedicated to democratizing access to modern data stacks for businesses of all sizes. Our team provides a unified platform that deploys, manages, and monitors your organization’s data infrastructure, making data analytics seamless and efficient. This approach saves companies the time and money to hire highly skilled DevOps engineers to deploy and maintain an effective data pipeline, significantly reducing the maintenance cost during system updates.  

With Shakudo, you get the flexibility to choose the desired data tools without having to confront compatibility challenges that often arise when different teams within an organization try to build their own data stack. The beauty of our platform lies in the fact that we manage the data stack as an evolving system that can be modified per request, ensuring that your data architecture remains adaptable and aligned with your evolving business needs at all times.  

To find out more about Shakudo’s services and how you can deploy data tools securely with no DevOps required, give our experts a call or schedule a demo.