A big data platform is a complex and sophisticated system that allows organizations to store, process, and analyze large volumes of data from various sources. It consists of multiple components that work together in a secured and governed platform. To meet the organization’s diverse and evolving needs, the big data platform must satisfy several requirements.
Data Ingestion:
– The platform should be capable of ingesting data from different sources, including databases, file systems, APIs, and data streams.
– It should support both batch and streaming modes of data ingestion.
– The platform should be able to read and write various file formats and table formats such as JSON, CSV, XML, Avro, Parquet, Delta Lake, and Iceberg.
– There should be provisions for defining data quality requirements, such as data completeness, accuracy, and consistency, and the ingestion pipeline should be capable of validating and cleansing the data accordingly.
Data Storage:
– The platform should provide reliable and high availability access to the stored data.
– It should ensure data durability through data replication and backup strategies to prevent data loss in case of hardware failures or errors.
– The platform should offer efficient storage and retrieval of data with low latency and high throughput.
– There should be elasticity in storage and management to handle growing data volumes by scaling up or down as required.
– Data lifecycle management capabilities should be available, including the ability to apply changes, add missing data, and revert to previous versions.
Data Processing in the Data Lake:
– The platform should be flexible to support multiple data types and formats and integrate with different distributed data processing and analysis tools.
– Data cleaning should be possible to remove errors, inconsistencies, and missing values from the data.
– Data integration should allow combining and integrating multiple data sources into a unified dataset, resolving schema or format differences.
– Data transformation should be supported to prepare the data for downstream processing or analysis, such as aggregation, filtering, sorting, or pivoting.
– Data enrichment capabilities should be provided to enhance the data with additional information for better context and insights.
– Data reduction techniques should be available to summarize or sample data while preserving essential characteristics and insights.
– Data normalization and denormalization should be supported to ensure consistent storage and improved performance.
Data Observability:
– Data validation should be performed to ensure the validity, accuracy, and consistency of the data according to the expected format and schema.
– Data lineage tracking should be implemented to identify any issues or anomalies in the data flow.
– Continuous monitoring of data quality should be carried out to detect anomalies or errors and raise alerts.
– The system’s performance should be monitored, including latency, throughput, and resource utilization, to optimize performance.
– Metadata management should be in place to manage data schema, dictionaries, and catalogs to ensure accuracy and currency.
Data Usage:
– The platform should provide user interfaces, both command-line and graphical interfaces, for data processing and visualization.
– Data access should be secure, with appropriate security controls and protocols in place to protect against unauthorized access or breaches.
– Data mining techniques should be available for exploratory data analysis, discovering patterns, relationships, or insights using statistical or machine learning algorithms.
– Data visualization capabilities should allow stakeholders to effectively communicate insights and findings using charts, graphs, or other visualizations.
Platform Security and Operation:
– The platform should ensure compliance with data governance policies and regulations, including data privacy, usage practices, retention policies, and access controls.
– Fine-grained access control should be supported to control data access and sharing based on management policies that consider specific characteristics and requirements.
– Data filtering and masking should be available to apply restrictions on sensitive data.
– Encryption at rest and in transit should be enabled using SSL/TLS.
– Integration with the corporate directory should allow seamless integration of users and user groups.
– The platform should be isolated in the network and accessed through a single entry point to establish a secure perimeter.
– An admin interface should be provided for configuring and monitoring services, managing data access controls, and governing the platform.
– Monitoring and alerts should be exposed to ensure the health and performance of services and applications.
Hardware and Maintenance:
– The platform should support both cloud and on-premise infrastructure options, considering the trade-offs between flexibility and scalability vs. control, security, and compliance.
– It should allow a separation between storage and processing resources and, in certain cases, enable collocation of processing and data.
– Provision of storage and computing infrastructure in line with the expressed data volumes and future usage requirements.
– Cost-effective storage and data management with consideration of storage costs, management costs, and overall total cost of ownership (TCO).
– Cost management practices and calculation of the TCO, taking into account various factors and platform specificities.
– User support should be available to assist platform users with skill acquisition, architecture validation, patch and feature deployment, and efficient resource utilization.
In conclusion, a big data platform should be flexible, resilient, and performant to meet the organization’s evolving needs. It should ensure data security, compliance, and quality while effectively communicating insights and findings to stakeholders. Cost-effective operation over time should also be a priority.