Data cloud refers to a centralized repository that allows you to store all your structured and unstructured data at any scale. It provides a single place to store, transform and analyze large amount of data in their original format without having to worry about data schemas. Unlike data warehouses, data is stored as-is without any alterations made to the organizational structure or formatting to support specific queries.

Key Characteristics of a Data Lake


Some of the key characteristics that define a data cloud include:

Raw and Unmodified Data


One of the core aspects of a data cloud is that it accepts and retains data in its native, raw format without needing to refactor it into predefined schemas. This allows you to store data from multiple sources without restrictions. Raw data can be in multiple formats like structured, semi-structured or unstructured.

Separation of Storage and Processing


Data clouds separate the data storage function from data processing and analytics workflows. This enables organizations to independently scale storage and compute resources based on business requirements. Processing capabilities are added incrementally without disrupting the storage architecture.

Self-Service Analytics


The data in a lake stays in its native form and enriched with additional context and schemas when needed. Business users and data scientists can directly access raw Data Lake  through analytical tools and APIs to perform experiments and analytics without IT intervention.

Integration of Multiple Data Sources


Data can be ingested from a variety of internal and external sources like databases, data warehouses, IoT sensors, social feeds, web servers into a centralized lake. It eliminates data silos and enables integrated analytics across the organization.

Support for Multiple Formats


Both structured and unstructured data like documents, videos, audio files, images, sensor data, along with traditional files like csv, json, xml can be stored in a lake in their original format. This universal data model maximizes flexibility and utility.

Cost Effectiveness at Scale


Large volumes of incoming raw data is simply added to the lake without worrying about fixed loads or pre-processing. Cloud-based data clouds deliver on-demand scalability and pay-as-you-go cost structure which makes them economical for diverse modern workloads.

Architectural Components of a Data cloud


Understanding the key architectural components that comprise a typical data cloud is important. Some important elements in a data cloud architecture are:

Ingestion Layer


The ingestion layer is responsible for collecting raw data from various sources using connectors and APIs. It centralizes streaming and batch delivery of incoming data to the data cloud storage in near-real time. Common methods of data ingestion include FTP, API integrations and change data capture.

Storage Layer


Massive volumes of raw data from multiple lines of business are stored here in their native formats using low-cost object storage like Amazon S3 or Azure Data cloud Storage. It provides unlimited scalability and inherent security at a lower TCO compared to a data warehouse.

Catalog Service


Catalog helps users to search, discover, understand and govern data from the lake. It maintains metadata information about raw datasets, schemas, semantics, transformations and quality without schema enforcement. Services like AWS Glue Catalog are commonly used.

Data Processing Layer


This distributed compute framework processes and transforms raw data on-demand using services like Spark, Hadoop, Flink etc.into structured datasets ready for analytics. Tasks like filtering, aggregation and ETL jobs are executed from the data cloud without moving data.

Analytics and Visualization Layer


Self-service BI tools, dashboards, notebooks and machine learning platforms access curated and modeled datasets from the lake to deliver insights. Lakes empower rapid experimentation with data science and democratize analytics. Popular tools used are Tableau, Power BI, Databricks, Sagemaker etc.

Governance and Security Services


Strong governance is critical to manage access control, auditing, data quality, lineage and ensure regulatory compliance of sensitive data. Lakehouses enhance security capabilities through features like encryption, access control lists, auditing and monitoring.

Big Data Workflow in a Data cloud


A typical workflow in a data cloud involves the following stages:

Raw Data Ingestion: Data from multiple sources like databases, apps, IoT devices is continuously ingested into the raw data zone of the storage layer using API/connectors.

Data Curation: Curators understand raw data semantics, structure it without enforcing schemas and tag with appropriate metadata to enable discoverability.

Data Transformation: Distributed processing jobs are executed on raw data in storage layer to normalize, validate, enrich and transform datasets into analytic-ready formats like ORC, Parquet etc. Tasks like filtering, joins and aggregations are performed.

Data Access: Business users, data scientists gain self-service access to curated datasets from the lake using catalog/API. They perform analytics, modeling and train/deploy ML models directly from storage.

Data Modeling: Machine learning algorithms are applied to datasets to automatically generate predictive models for predictive analytics projects.

Data Visualization and Consumption: Insights are delivered from the lake to end users through interactive visualization tools, dashboards without moving the underlying data.

change Data Capture Streaming ingestion with change data capture continually updates target datasets with incrementally added or changed raw data while maintaining analytics-ready views. Data clouds are revolutionizing how organizations store, process, analyze and monetize data at massive scale. Here are some key benefits an enterprise data cloud.

 

 

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About Author:

Alice Mutum is a seasoned senior content editor at Coherent Market Insights, leveraging extensive expertise gained from her previous role as a content writer. With seven years in content development, Alice masterfully employs SEO best practices and cutting-edge digital marketing strategies to craft high-ranking, impactful content. As an editor, she meticulously ensures flawless grammar and punctuation, precise data accuracy, and perfect alignment with audience needs in every research report. Alice's dedication to excellence and her strategic approach to content make her an invaluable asset in the world of market insights.

(LinkedIn: www.linkedin.com/in/alice-mutum-3b247b137  )