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The term data stack refers to various technologies an organization uses to process raw data for analysis. Since the invention of computers, the data stack has evolved and gone through multiple generations, each with unique characteristics and approaches to data storage and processing. The earliest days saw companies stacking data storage servers in their data centers. The latest emerging model is the data fabric, a cloud-based, decentralized solution that supports low/no-code data integration and processing. 

In this article, we discuss the various generations of the data stack, from on-premise to cloud-based to distributed and delve into the current generation—data fabric. Additionally, we discuss best practices for creating a  data stack that leverages the strengths of each generation.

The data stack evolution

The need to manage data efficiently and cost-effectively has driven the development of data stacks.

  1. The first-generation data stack was on-premise, where data was stored and processed locally on physical hardware. It was designed for use by IT and Engineering teams.
  2. The second-generation data stack centers around  cloud-warehouse with a focus on analytical use cases. Commonly referred to as the “Modern Data Stack”, it consists of point tools with simple workflows that enable self-serve for analysts and non-engineers.
  3. The third-generation data stack is distributed and multi-speed where data is stored and processed across multiple nodes in a distributed architecture. 

However, the most recent emerging model is the fourth-generation data stack, which includes data fabric. In this model, you abstract data from underlying storage and computing infrastructure and present it as a unified platform. 

Data stacks of the future

As the data stack continues to evolve, future generations may include intelligent, serverless, and multi-speed  solutions. You can:

  • Analyze and use data to make automated decisions through machine learning and artificial intelligence.
  • Process and store data without server management or infrastructure provisioning.
  • Use an immutable and decentralized ledger that ensures data integrity and security. 

Summary of data stack generations 

Historical Perspective: The Evolution of the Data Stack

Before delving into the intricacies of the modern data stack, it’s essential to understand its evolution. Over time, data stacks have adapted to the changing needs of businesses, from local storage solutions to complex, distributed systems.

Understanding each generation’s characteristics is important for companies looking to create a modern data stack that meets their unique needs and requirements.

Generation Characteristics Description
First Generation On-premise Data is stored and processed locally on physical hardware.
Second Generation Cloud-based Data is stored and processed in a cloud-based environment.
Third Generation Distributed Data is stored and processed across multiple nodes in a distributed architecture.
Fourth Generation Data Fabric  Data is abstracted from underlying storage and computing infrastructure and presented as a unified fabric.
Future Generations Intelligent, Serverless Multi-speed Data is analyzed and used to make automated decisions through machine learning and artificial intelligence, without the data integration team having to worry about  infrastructure provisioning. Infrastructure would be provisioned and scaled dynamically. In addition the data processing will be multi-speed (batch, stream, real-time) dynamically and intelligently allocated depending on the goals of the data task at hand.

The components of a modern data stack

Deciphering the Modern Data Stack

A comprehensive modern data stack isn’t just about storage. It’s about ensuring data is ingested, integrated, maintained, visualized, and used efficiently. Let’s break down the components.

The modern data stack comprises four key components, summarized in the table below.

Components  Description
Ingestion and integration These tools extract, transform, and load (ETL) data from various sources into a centralized repository.
Quality and governance These tools validate data for accuracy, consistency, and conformation to specific standards.
Storage and management These tools store and manage large volumes of data in a centralized repository. 
Visualization and analytics These tools allow users to create visualizations and analyze integrated data. 

#1 Ingestion and integration

Data ingestion and integration tools are responsible for extracting data from various sources and transforming it into a standardized format for storage and analysis. You can use them to integrate data from disparate sources, such as databases, APIs, and files, and transform the data into a format suitable for further processing and analysis. It is vital to choose the data ingestion and integration tools that support:

  • Various data sources, including structured, semi-structured, and unstructured data.
  • Efficient and robust data transformation, including data cleaning, normalization, and enrichment.
  • Scalable architecture so you can process large data volumes efficiently.
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#2 Quality and governance

Data quality and governance tools ensure your data is accurate, consistent, and conforms to security standards and policies. You can use them to establish data quality rules and monitor real-time data. These tools can detect anomalies, identify missing values, and reconcile inconsistencies. To implement best practices for data quality and governance, organizations must:

  • Define data quality rules and establish security policies. 
  • Establish procedures for data profiling, metric collection, and data cleansing. 
  • Classify data for security and compliance. 

#3 Storage and management

Data storage and management tools provide the infrastructure for storing, processing and managing large volumes of data. You can collect and retrieve data efficiently by  choosing cloud-based storage solutions that support:

  • Data backup and disaster recovery. 
  • Security features like access control, encryption, and auditing.
  • Scalability and cost-effectiveness.

#4 Visualization and analytics

Data visualization and analytics tools enable users to create visualizations and analyze integrated data. You can gain insights, discover hidden patterns and make data-driven decisions. You should choose tools that support:

  • Interactive data visualization, including dashboards and reports. 
  • Self-service analytics that allows users to create custom reports. 
  • Data exploration and predictive analytics 

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Critical data integration components in the modern data stack

The Often Overlooked: Data Integration Essentials

Products focused on data integration quality are often overlooked in a modern data stack. Many organizations focus on downstream tools such as BI and analytics platforms for data insights and decision-making rather than the underlying data integration processes. However, with the increasing demand for real-time data insights, scalable data processing is becoming more critical in the modern data stack. 

While the allure of advanced analytics or massive storage solutions is tempting, data’s real value is realized only when it’s correctly integrated. Consider these pillars:

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Data fabric

Data fabric is a system that connects and integrates data from various sources into a unified data view. A centralized governance model provides real-time access, integration, and data analysis. Organizations can easily manage and monitor data pipelines, ensuring data accuracy and consistency.

  • Conceptual Depth: Data fabric is not just a mere tool; it’s an architecture and a set of data services. It provides seamless data access, discovery, transformation, integration, and delivery capabilities across various data sources, locations (on-premises, cloud, edge), and formats (structured, unstructured).
  • Technology Stack: Modern data fabric solutions leverage advanced technologies like machine learning, data virtualization, and metadata scanning to provide dynamic data integration capabilities.
  • Operational Aspects: Through data fabric, organizations can handle aspects such as data lineage (understanding the source and flow of the data), versioning (tracking different versions of datasets), and security (ensuring only authorized access).

ETL

Extract, Transform, Load (ETL) is a common practice for extracting and processing data from various sources. It involves removing data, transforming it to fit the target data model, and loading it into a target system. Similar methods, such as ELT, also exist where the load happens before the transformation.

Phases Elucidated:

  • Extract: This phase focuses on pulling data from varied source systems, which can be databases, ERP systems, flat files, streams, APIs, and more. The raw data could be JSON, XML, Parquet, relational tables, logs, or formats.
  • Transform: Here; data undergoes a series of operations to convert it into a format suitable for analytics and reporting. These operations can be as simple as datatype conversion or as complex as windowed aggregations, joins, and pivots.
  • Load: Post-transformation, data is loaded into a destination system which can be a data warehouse, data mart, or another database system.
  • ELT Alternative: The extraction remains the same in ELT, but loading occurs before the transformation. Here, the target system does the heavy lifting, usually a robust data warehouse optimized for processing large datasets, such as Snowflake, Redshift, or BigQuery.

Data Catalog

Data cataloging is creating a centralized inventory of all the data assets in an organization. You collect metadata, such as data types, sources, and relationships, and organize it for technical and non-technical access. Data cataloging allows organizations to find and use their data quickly and efficiently.

Technical Layers:

  • Metadata Repository: A central repository stores metadata extracted from diverse data sources. Depending on the scalability and search requirements, this repository can be relational, NoSQL, or graph-based.
  • Scanners/Crawlers: These automated programs scour data sources, extract metadata, and push it to the central repository.
  • Search & Discover: Powered by search algorithms and often machine learning models; this layer aids users in finding the required datasets.
  • Collaboration & Annotation: Modern catalogs offer collaborative features where users can annotate, comment, and rate datasets, increasing trustworthiness.

Governance & Compliance: Catalogs also play a vital role in enforcing data governance policies, ensuring compliance with regulations like GDPR or HIPAA by tracking personally identifiable information (PII) or sensitive data.

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Data quality

Data quality is a set of practices, tools, and technologies that ensure your data is accurate, complete, consistent, and reliable. Data quality tools identify and correct errors, inconsistencies, and redundancies in data. By improving your data, you can make informed decisions based on reliable data.

Framework Aspects:

  • Profiling: This involves statistically analyzing the content of a dataset to understand its structure, content, and quality.
  • Validation: Data is checked against predefined rules and patterns, ensuring it meets the necessary standards.
  • Cleaning: Erroneous data is rectified or removed based on business rules.
  • Monitoring: Continuous checks are performed on incoming data, ensuring ongoing conformance to quality standards.

Technological Landscape: Modern data quality tools use machine learning and AI to predict and rectify anomalies, auto-detect data types, and auto-generate quality rules.

While it’s alluring to gaze at the shimmering peaks of data analytics and insights, the actual trek is in the intricate valleys of data integration. Properly integrating data isn’t a mere technical necessity; it’s an act of setting a clear vision, ensuring that the data-driving decisions are rooted in quality, consistency, and accessibility.

Nexla: A deep-dive into the technical underpinnings

Choosing a tool for a data engineer is about more than surface-level offerings. It’s about understanding the nuts and bolts, ensuring the tool scales and aligns with the intricate requirements of modern data operations. Let’s peel back the layers of Nexla and delve into the profound technical intricacies that make it stand out.

In the intricate world of data, Nexla stands out. By encapsulating the essentials of data integration and governance into a no-code platform, data operations are democratized, ensuring that businesses, irrespective of their size or expertise, can harness the true power of their data.

Architecture & infrastructure:

  • Distributed System Design: Architected for distributed processing, ensuring that massive data loads can be processed in parallel, optimizing for speed and resilience.
  • Containerization & Scalability: Ensures its services can scale horizontally, adapting to fluctuating data workloads and ensuring optimal resource utilization.
  • Hybrid Integration: Provides connectors for on-premises, cloud, and hybrid environments. Its sophisticated connection handlers ensure minimal latency, high throughput, and fault tolerance.

Advanced data processing:

  • Parallel Data Streaming: Simultaneously process data streams, leveraging parallel processing paradigms to ensure real-time or near-real-time data integration and transformation.
  • Custom Data Transformations: While it’s a no-code and low-code platform, Nexla doesn’t restrict advanced users. Data engineers can plug in custom transformation logic using Python or Java to perform intricate data manipulations.
  • Optimized Data Shuffling: Moving data between nodes (shuffling) can be expensive in distributed data operations. Nexla has methods in place to minimize data shuffling and optimize for performance.

Metadata management & data lineage:

  • Dynamic Metadata Extraction: Automatically extract, catalog, and manage metadata from various sources. This automated metadata extraction makes data discovery and classification seamless.
  • Data Lineage Tracking: Understanding the origin and transformations applied to data is crucial for complex data operations. The platform provides comprehensive data lineage visualization, ensuring transparency and traceability.

Advanced governance & security:

  • Role-Based Access Control (RBAC): Allows granular control over data access, ensuring that users can only access data they’re authorized to view. It aligns with the principle of least privilege, bolstering security.
  • Audit Trails: Every operation, from data ingestion to transformation to export, is logged in an immutable audit trail, providing a clear record for compliance and security audits.
  • Hashing: This cryptographic function converts input data (of any size) into a fixed-length string. It’s one-way, meaning you can’t reverse a hash back into its original data. This differs from encryption, where you can decrypt data with the appropriate key to its original form. Hashing is commonly used for passwords, for instance.
  • Encryption: This is converting data into a code to prevent unauthorized access. Encrypted data can be decrypted back to its original form with the appropriate decryption key.
  • Slicing Specific Segments of Data for Specific Users: This can be understood as a form of data masking, where only specific segments or portions of the data are revealed to certain users. Data masking doesn’t change the original data but limits the view or exposure of sensitive data to unauthorized users.

No-code flexibility with code-level precision:

  • Drag-and-Drop Transformations: For users who need to be better-versed in coding, no-code and low-cde platforms provide intuitive drag-and-drop interfaces to define data transformations, ensuring they can efficiently process data without deep technical know-how.
  • API Extensibility: For more complex, tailored operations, Nexla offers robust APIs. Data engineers can leverage these APIs for custom integrations, automation, and extensions.

Nexla is one of the few data products that incorporates these specialized components into an easy-to-use no-code platform. It provides a simplified and streamlined approach to data integration and governance, reducing the complexity and increasing the efficiency of the data management process.

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Conclusion

Building a future-proof data stack requires integrating four critical components—ingestion, governance, storage, and visualization. By implementing best practices for each component, organizations can build a scalable, efficient, and capable data infrastructure that meets their data needs. However, most organizations emphasize visualization and storage—even though integration and quality determine the reliability of your data and analytics. You have to implement critical data integration components for long-term success. Nexla, is one of the few products that provides all critical integration and governance components in a single solution.

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