Data management: crafting the bedrock

At the heart of a compelling data governance strategy lies proficient data management. This sphere encompasses the meticulous aggregation, fusion, orchestration, and retention of trustworthy datasets, acting as a catalyst for businesses to harness maximum value. With the contemporary business landscape rapidly evolving, an organization’s merit hinges significantly on its prowess in extracting insights from the wealth of data it stewards. Moreover, data management plays a pivotal role in furnishing organizations with insights on data interaction frequency, alongside offering a suite of tools tailored for holistic data life cycle oversight.

Historically, the bastion of analytics-driven data management has been the data warehouse, typified by tabular data that is managed via structures such as tables and views comprising rows and columns. Conversely, data lakes stand as reservoirs for an eclectic mix of structured or unstructured data tailored to data science or ML pursuits. From raw text files and Apache Parquet formats to multimedia content, such as images or videos, data lakes manage this myriad of datasets at the granular file echelon.

Enter the realm of the data lakehouse—a new paradigm where organizations can stockpile data singularly, making it accessible for a spectrum of analytical applications. This innovative approach curtails data redundancy and pares down the data management ambit for organizations.

Data ingestion – the gateway to information

Before data finds its permanent abode for subsequent utilization, it undergoes the critical phase of collection. While data sources are myriad, the principal conduits include the following:

• Cloud-based storage systems
• Message relay channels
• Traditional relational databases
• APIs of software as a service (SaaS) platforms

Lately, a significant chunk of data has been sourced from files relayed to object storage facilities that are integral to public cloud service providers. These files, ranging from a handful to millions (daily), encapsulate a diverse array of formats:

• Unstructured entities such as PDFs, audio files, or videos
• Semi-structured formats such as JSON
• Structured types, including Parquet and Avro

For those venturing into the realm of data streaming, distributed message queues, such as Apache Kafka, emerge as the go-to platform. This seamless integration paves the way for ultra-fast message processing in a linear sequence. By complementing open source queue systems, each marquee cloud provider boasts its native message relay service.