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Data fabric vs data lake:
comprehensive comparative analysis & tools
March 28, 2025
Data fabric and data lake are two popular approaches to storing enterprise data, organizing data management and data integration processes, and regulating how an organization’s data is governed.
While data lake and data fabric are separate concepts, in certain situations, they can be used in tandem, with
a data lake used as a reservoir for keeping raw data and a data fabric unifying data pipelines and disparate
data environments to facilitate data access across the company.
Providing data services for over 15 years, Itransition offers end-to-end implementation,
optimization, consulting, and support of data management, analytics, and governance solutions and workflows.
Data fabric vs data lake: market trends
80%
of data remains unused by businesses due to its unstructured format
30%
CAGR at which the data fabric market will grow between 2024 and 2032
25.3%
CAGR at which the data lake market size will expand from 2023 to 2030
Main components of a data lake
Data lakes are schema-agnostic centralized repositories that store structured, semi-structured, and unstructured data in its original format. This information then can be used for different business purposes, such as machine learning processing, backup and archiving, big data analytics, etc. Here are the typical layers of a data lake architecture.
Scheme title: Functional data lake architecture
Data source: researchgate.net — Data
Lakes: A Survey of Concepts and Architectures, 2024
Data sources
Data lakes collect information scattered across heterogeneous sources containing business data. These can include transactional (NoSQL/SQL) databases, web and SaaS applications (ERP, CRM, marketing automation, customer service, HR, and other tools), file sharing systems, and streaming data sources (IoT, sensor devices, social media, real-time analytics tools).
Data ingestion
This is where information is ingested from a variety of sources and enters a landing zone where it can be temporarily stored in an as-is state.
The landing zone can be omitted in case a company has established continuous ingestion, extraction, transformation, and loading (ETL) as well as change data capture (CDC) capabilities.
Data storage
At this layer, data is categorized and stored.
As soon as it’s inside the lake, each set is assigned a unique indicator, or an index, and a metadata tag to speed up queries and help users quickly look up the requested data.
Data transformation
Data undergoes cleansing, deduplication, reformatting, enrichment, or other necessary operations and is then moved to the trusted zone for permanent storage.
Analytics sandboxes
These are optional separate environments isolated from the main data storage and transformation layers where data scientists can explore the data.
Data consumption & interaction
Here, employees can access the refined data through business intelligence tools and dashboards and use it to build reports and dashboards. Alternatively, data undergoes another ETL round and transferred to the data warehouse for later processing.
Data governance
To guarantee the quality, safety, availability, and timeliness of information, companies typically establish a data governance framework as an overarching layer.
A data fabric architecture
Data fabric is a design approach to data management that allows companies to have a unified view of data kept in various sources without transferring it to a centralized location. A data fabric connects these sources through a combination of data integration, data governance, and data cataloging tools. Here are the primary building blocks of a data fabric architecture.
Data management
A core component of a data fabric, the data management layer represents a set of practices that guarantee data governance, security, quality, and lineage.
Data integration
The data virtualization layer consolidates data regardless of its type, volume, and location without moving it and creating numerous copies.
Besides that, to ensure data integrity, data fabric can employ ETL, CDC, stream processing, etc.
Data processing
At this staging area, raw data is refined and filtered to be used for future querying and data analysis tasks.
Data orchestration
At this stage data is transformed, integrated, and cleansed in line with the requirements set by target data storage or software systems.
Data discovery
This component enabled data modeling, virtualization, and curation, allowing data scientists and business users to identify hidden trends, anomalies, and relationships within data.
Data access
This layer is represented by business intelligence tools, self-service analytics, and other data visualization solutions enabling users to access and use the data they need.
Looking for a vendor to deliver a tailored data lake solution?
Data lake vs data fabric: key differences
Here is a multi-faceted examination of both approaches, highlighting their major differentiators, strengths, and weaknesses.
Data lake | Data fabric | |
|---|---|---|
Purpose | Centralized storage of large data volumes | Seamless integration and management of data across different environments |
Data structures | Format-agnostic and can store structured, semi-structured, and unstructured data | Brings diverse data types to an orderly format across different environments (data lakehouses, data lakes, data warehouses, databases, real-time data streams, etc.). |
Data governance & security capabilities |
| Centralized governance (access, masking, and data quality policies, etc.) is automatically enforced across all datasets via knowledge graphs, data integration, AI, and metadata activation capabilities, ensuring consistent policy adherence. |
Data integration capabilities | Since data lake focuses more on data ingestion rather than data integration, ensuring data consistency can require additional processing and transformation steps. | Data fabric’s advanced data integration features allow for the instantaneous or near-instantaneous integration of data from diverse sources. |
Scalability | Inherently scalable in terms of storage capacity | Allows for horizontal and vertical scaling, providing agility and flexibility across all components |
Implementation complexity | More straightforward to implement | More challenging setup |
Benefits |
|
|
Limitations |
|
|
Use cases | Advanced and big data analytics and machine learning, IoT and sensor data analysis, log data analysis, forecasting, and real-time anomaly detection in data sets | Enterprise and operational intelligence, 360-degree customer view, data management process consolidation and automation, progressive data consolidation, de-silos, self-service data marketplace development |
Data fabric vs data lake: what to choose
The choice between a data fabric and a data lake depends on multiple factors that businesses should carefully consider. The key ones include the existing data strategy, specific data needs, available technical, human, and financial resources, data security and compliance requirements, the desired frequency of data ingestions, current workloads, and long-term business objectives.
Reasons to opt for a data lake
- You need to collect and store vast amounts of raw data in its native format with the possibility of analyzing it in the future
- You undertake exploratory data analysis as well as big data processing and machine learning initiatives
- You seek a more straightforward and cost-effective approach to raw data storage
When to choose a data fabric
- You require real-time or near-real-time data integration
- You need unified access to a wider range of data
- You want to centralize and automate data governance
Consider a hybrid approach
A data lake and a data fabric can effectively co-exist within one data ecosystem, amplifying each other’s benefits and capabilities and creating a modern data architecture with holistic data management where:
- The data lake functions as a scalable raw data storage foundation and can be one of the elements overseen by a data fabric.
- The data fabric integrates data from a data lake together with information from other sources within a larger data landscape, facilitates necessary data management and governance, and enables real-time analysis and decision-making.
Data mesh: a rising architectural framework
While data fabric and data lake are two prominent technologies in the context of data management, data mesh is
another promising concept gaining traction these days.
Data mesh is a modern analytical data architecture and operating model characterized by
decentralized ownership and data governance. It allows different business departments, such as marketing,
sales, and finance, to build data products tailored to their needs. This approach emerged in 2019, has been
developing in the last five years, and was named an Innovation Trigger in the Gartner 2024 Hype Cycle for
Emerging Technologies.
Expectations
Plateau will be reached:
<2 yrs.
2-5 yrs.
5-10 yrs.
<10 yrs.
Time
Scheme title: Consumer perception of AI assistant usefulness by task and generation
Data source: Zendesk
Primary principles
A departure from a centralized repository like a data warehouse, data lake, or data lakehouse, the concept is based on four pillars: decentralized data ownership, data as a product, self-serve data platforms, and federated computational governance. Data mesh provides distributed data models for each domain to manage its own data, pipelines, storage, and APIs end-to-end together with a set of principles that can guide the design of domain-specific data products and governance processes.
Benefits
Since data mesh architecture is a distributed one, the solution can handle the organization’s fluctuating
data volumes and the needs of different departments. Moreover, a data mesh simplifies data usage and sharing
for teams, as they can work directly with their own data without centralizing corporate information.
Domain ownership stipulated by the data mesh design enhances accountability, as each department
is responsible for data quality, discoverability, and security. Plus, teams can align data management with
their unique needs, creating customized data products and implementing tailored processes.
Drawbacks
Data mesh implementation can be fraught with several challenges for the organization. First and foremost, businesses have to adopt the decentralized data ownership approach, which can lead to inconsistent data management practices across the organization, data silos, misinformation, and inaccurate data interpretation. As a result, a data mesh can be too effort-intensive to maintain, requiring both IT team expertise and employee buy-in.
Use cases
A data mesh is a powerful and innovative approach that can be used in various scenarios, primarily for augmenting data analytics, as data products are created specifically for analytical consumption. Some of its use cases include:
- Generating customized reports
- Data pipeline customization
- Data silos prevention
- Personalized product development
- Fraud detection
- Regulatory reporting
- Third-party data integration
Find out what data management framework works best for you
Top platforms to consider for building data lakes
Features
- Designed to work with Hadoop and all frameworks that use the Apache Hadoop Distributed File System (HDFS)
- Compatibility with Azure Databricks, Azure Synapse Analytics, or Azure HDInsight for data processing and Microsoft Power BI for data visualization
- Built on Azure Blob Storage with capabilities such as automated lifecycle policy management, object-level tiering, and diagnostic logging
- Optimized specifically for big data analytics
- Hierarchical namespace feature that ensures high-performance data access
- Azure role-based access control (Azure RBAC), Portable Operating System Interface for UNIX (POSIX) access control lists (ACLs), data encryption at rest with Microsoft-managed or customer-managed keys
- No limits on account sizes, file sizes, or the amount of data that can be stored
- Replication models for data redundancy with locally redundant storage (LRS) and Geo-redundant storage (GRS)
Category
A set of capabilities dedicated to big data analytics
Features
- Seamless integration with Google Cloud services like Dataflow and Cloud Data Fusion for data ingestion, Cloud Storage for storage, and Dataproc and BigQuery for data and analytics processing
- Auto-scaling services, allowing storage to be separated from computation to speed up queries and control costs per gigabyte
- Real-time and batch data processing support using SQL, Python, R, and other languages, as well as third-party tools
- The ability to re-host on-premises data lakes on Google Cloud
- Compatibility with data science and analytics tools like Apache Spark, BigQuery, AI Platform Notebooks, and GPUs
- Identity and access management (IAM), granular access control, policy propagation, metadata security, encryption for data at rest and in transit, and DDoS protection
Category
Object storage for data lakes
Features
- Native integration with the powerful IBM Cloud ecosystem
- High throughput with features like 18 TB SMR drives and optimized read/write speeds
- 99.999999999999% of data durability, multi-region support, data replication across regions, object versioning to keep several versions of an object in a bucket
- Data encryption at rest and in transit, S3 Object Lock to prevent objects from being deleted or overwritten, role-based policies and access permissions, compliance with SEC17a-4f requirements
- Information dispersal algorithm (IDA), ensuring system reliability, availability, and storage efficiency
- Custom search and insights
- Efficient data lifecycle management, allowing an object or objects from a bucket to be automatically deleted, as well as automatic failover, data rebuild, auto expansion, and rebalancing
Category
Durable storage for unstructured data
Features
- 99.999999999% annual data durability thanks to storing each object redundantly across different domains, monitoring data integrity using checksums, and automatically detecting and repairing corrupt data
- Automated object archiving and deletion, minimum 90-day retention requirement
- Tight integration with Oracle Cloud Infrastructure Identity and Access Management, leveraging SSL endpoints and the HTTPS protocol, data encryption by default using 256-bit Advanced Encryption Standard (AES-256)
- The ability to tag objects with multiple user-specified metadata key-value pairs
- Asynchronous data replication between buckets
- Multipart upload functionality with Oracle Object Storage native API, the Oracle Cloud Infrastructure (OCI) Software Development Kits (SDKs), the OCI Command Line Interface (CLI), and the OCI Console
Category
Storage for raw and unstructured data
Features
- Unified, multi-cloud data lake compatible with existing ADLS Gen2 applications, including Azure Databricks, Synapse Analytics, Azure Storage Explorer, and Azure HDInsight, among others, as well as Amazon Web Services (AWS)
- Direct Lake mode that maintains real-time data consistency and eliminates the need for manual data refreshes
- Consolidated data across domains, clouds, and accounts presented through shortcuts
- Tenant-level and out-of-the-box governance, such as data lineage, catalog integration, certification, data protection, etc.
- The OneLake file explorer feature for Windows that simplifies navigation across workspaces and data items, easily uploading, downloading, or modifying files
- The ability to build Power BI reports directly on top of OneLake
- Data access roles, shortcut security, Microsoft Entra ID for authentication, encryption of data at rest and in transit, private links, external access restrictions, and audit logs to track user activities within OneLake
- Zone-redundant storage (ZRS) and locally redundant storage (LRS), fault tolerance, disaster recovery, and a default retention period for deleted data
Category
A unified data lake integrated into the Microsoft Fabric toolset
Features
- Native compatibility with AWS services, including AWS Lake Formation for quick data lake creation, AWS Glue for seamless data movement, and Amazon EMR and Amazon Athena for simplified data lake querying
- Connections to tens of thousands of partners through the AWS Partner Network (APN)
- Real-time and batch data support, collecting and moving data in its original format
- A streamlined and centralized data catalog to manage metadata and data permissions in one place
- Fine-grained tag-based or name-based data access controls, database-, table-, column-, row-, and cell-level permissions, metadata management, and data governance centralization
- AI and ML capabilities to build and train deep learning and ML models, summarize query results, and get insights by asking questions in natural language
- Data replication for enhanced availability and durability
- The ability to store data in multiple data centers across three availability zones within a single AWS Region to prevent specific data center problems
Category
A cloud object storage
Features
- Automated data lake creation for cloud, hybrid, and on-premises environments
- 140+ data source connectors, including Microsoft Azure, Snowflake, Amazon, Google Cloud, Databricks, SAP, etc., with the ability to rapidly develop new connectors
- Zero-code, model-driven approach to data lake creation and management
- Automatic standardization and combination of change streams with the Qlik Compose for Data Lakes feature to make raw data usable for analytics and other needs
- Agentless, log-based approach to change data capture for real-time data ingestion and replication
- End-to-end lineage, a metadata-powered catalog, providing a governed data marketplace to discover, preview, and shop for curated data, and a Talend Trust Score™ feature for maintaining data quality and privacy
- Natural language and generative AI capabilities
- Qlick Enterprise Manager that allows you to efficiently design, execute, manage, monitor, and analyze data replication processes
Category
A BI platform that supports data lake creation
Features
- 145+ data connectors for cloud and on-premises data sources
- Integrations with Power BI, Azure Synapse Analytics, Azure Data Factory, and Microsoft 365
- Automated report page creation with the AI-powered Copilot feature
- Embedded generative AI capabilities, allowing employees to ask questions and get answers in natural language
- 300+ data transformation capabilities for data joins, aggregations, cleansing, etc.
- Row-level security (RLS) and object-level security (OLS), Purview-powered governance, inherited data sensitivity labeling, and automatic permissions application
- Unified experience with OneLake and lakehouse architecture integration
- Azure AI Foundry’s advanced AI and machine learning capabilities, enabling the creation and deployment of AI models
- Consolidated data discovery that streamlines its access, sharing, and governance
Category
An AI-enabled analytics platform with data fabric functionality
Features
- Deep integration with other Azure services such as Power BI, CosmosDB, and AzureML
- Compatibility with Apache Spark for big data preparation, data engineering, ETL, machine learning, fast start-up, and autoscaling
- Azure Synapse Data Explorer feature that enables an interactive query experience and efficient log analytics
- Data ingestion support from 90+ data sources
- Code-free ETL with data flow activities
- Serverless and dedicated resource models
- Built-in streaming capabilities
- Machine learning models to integrate AI with SQL and score data using the T-SQL PREDICT function
- Parquet, CSV, TSV, and JSON files support
- The ability to orchestrate notebooks, Spark jobs, stored procedures, SQL scripts, etc.
- AI-enabled pattern recognition, anomaly detection, forecasting, etc.
Category
Integrated analytics service for big data and data warehousing
Features
- A unified inventory of Google Cloud resources, such as BigQuery, and other resources, including on-premises ones
- The ability to build a domain-specific data mesh using data stored in multiple Google Cloud projects without data migration
- IAM permissions and roles to regulate who can perform actions on the Dataplex resources in the project
- AI artifact enrichment capabilities to add ownership, key attributes, and relevant context to files
- Single search experience with semantic search powered by Gemini that supports natural language queries
- Auto data quality, data profiling, and data lifecycle management to measure data quality and analyze data more effectively
- End-to-end data lineage to track how data moves through systems
- Dataflow templates to perform common data processing tasks like data ingestion, processing, and managing the data lifecycle faster
Category
AI-powered data fabric for unified data management
Features
- Connectivity with a variety of data sources, including Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, and Amazon Relational Database Service (Amazon RDS)
- Multiple ways to author ETL jobs, including Python shell jobs, Apache Spark jobs, AWS Glue streaming ETL, and AWS Glue Studio
- G.1X, G.2X, G.4X, G.8X, G.025X, and Standard worker types that are optimized for various workloads
- AWS Glue Data Catalog component that stores information about data formats, schemas, and sources and consists of databases, tables, crawlers, classifiers, connections, and Schema Registry
- AWS Glue DataBrew component with 250+ prebuilt transformations for no-code data preparation
- Generative AI capabilities to automatically analyze Spark jobs and generate upgrade plans to newer versions, as well as quickly identify and resolve issues in Spark jobs
Category
Data integration service with cataloging features
Features
- 30+ data sources support, including relational databases, files, applications, mainframe databases, and others, including Oracle, DB2, SQL Server, Sybase, Informix, ODBC, and XML
- Code-free agile data integration
- Visual tools to manage and track complex data flows
- Built-in, time-based or event-based scheduler for data integration workflow scheduling
- Data profiling to validate business rules, data assumptions, and source data anomalies
- An intuitive, dynamic visual map of data flows and dependencies for simplified metadata management
- Parallelizing data processing for robust performance
- One-click prototype-to-production capabilities to speed up data integration projects
- A unified web-based administration console for operating PowerCenter Services, managing user security privileges, and checking service logs
- Granular access privileges and flexible permission management via an enterprise directory system that uses either PowerCenter or LDAP authentication
Category
A cloud-based intelligent data management platform
Features
- Data transformation capabilities, including filter, flatten/normalize, aggregate, replicate, look up, join, and time windowing
- Unified interface for batch and streaming pipeline design
- AVRO, JSON, Parquet, Excel, CSV data format support
- Dynamic schema, reusable Joblets, and reference projects
- Wizards and interactive data viewer
- Automatic documentation generation
- Talend Trust Score feature that provides dataset reliability assessment
- Semantic discovery with automatic detection of patterns
- Cleanse data, mask data, and data matching on Spark and Hadoop
- Fraud pattern detection using Benford's Law
- Advanced statistics with indicator thresholds
- Cloud and on-premises licenses
- WS policy-based web services security
- Support for Apache Spark Batch and Apache Spark Streaming, Spark Universal, Spark on YARN platforms, server-less platforms, and dynamic distributions
Category
End-to-end data management platform
Features
- An API library and 100+ connectors to relational, operational, analytical, SaaS applications, files, and other data deployed in the cloud, on-premises, at the edge, or on a hybrid solution
- Support for structured and unstructured data and diverse storage solutions, including data lakes or warehouses, like Amazon Redshift, Google Big Query, Databricks, Snowflake, and Microsoft Azure SQL Data Warehouse
- A Tableau Prep Builder for self-service data preparation, providing a visual and direct way to combine, shape, and clean data without writing code
- An analytics catalog that shows all data in the Tableau ecosystem, as well as metadata, context, and lineage tracking
- Self-service visual and direct data transformation
- Metadata-driven automation and optimizations
- AI- and ML-based data preparation and data quality processes
- Row-level security and virtual connections to manage and share access to groups of tables
Category
A BI system with data fabric capabilities
Itransition’s data services
Data management
We help companies efficiently organize, store, and analyze data by setting up data pipelines, deploying data storage and management systems, and implementing comprehensive data governance frameworks.
Data warehousing
We assist businesses with implementing data warehousing solutions, building them on top of popular DWH platforms to create a single source of truth where corporate data is stored in a structured and organized format.
Data analytics
We deliver analytical solutions for the whole company or different business units, enabling decision-makers to keep track of the company’s performance, processes, and results.
Big data services
We offer a full scope of big data services, from strategy consulting and data management to big data analysis and interpretation, to assist businesses in handling large amounts of data and getting insights from it.
Data science
We enable organizations to extract meaningful insights from large datasets by implementing computer engineering, statistics, and advanced analytics tools, as well as innovative technologies like AI, ML, and computer vision.
Optimize your data workflows with an up-to-date data architecture
It’s hard to name a winner in the data fabric vs data lake debate since they both have their pros and cons and,
more importantly, serve different purposes. Moreover, they can be used as complementary solutions to strengthen
your data management strategy.
If your current methods of managing data with a data lake and data warehouses fail to deliver the needed result,
consider revamping your data management infrastructure into a data fabric. Your current data repositories will remain
essential components of your data landscape, but the more modern data fabric approach will bring more agility into
business operations. And with expert help from Itransition’s seasoned data engineers, you can get a well-built architecture
tailored to your business case.
Maximize your data value with Itransition
FAQs
A data lake is a storage repository where structured, semi-structured, and unstructured information resides in its as-is format. In turn, a data fabric is an innovative approach to data platform architecture that streamlines data access and management through the integration of data across different environments. A data mesh, in the meantime, is an analytical data architecture and operating model that decentralizes data ownership, granting authority to particular teams over their data domains.
A data lakehouse is a platform that combines the capabilities and advantages of an enterprise data warehouse and a data lake, such as the flexibility, cost-efficiency, and scale of data lakes, as well as data warehouse performance, data management, ACID transactions, and governance capabilities. It provides both advanced features and conventional data analytics solutions. However, since a data lake lacks centralized data governance, its adoption can lead to fragmented and siloed data swamps or cause data inconsistency or integrity issues.
Unlike a data lake, a data warehouse doesn’t support unstructured data in raw format. Instead, it arranges data according to a predefined schema before writing it into the database and makes the historical information available for reporting, business intelligence, and decision-making. A data lake, on the other hand, allows you to store and explore vast amounts of unstructured or rapidly changing data. Still, it requires additional efforts to ensure data quality, governance, and security so as not to become a data graveyard.
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