Now is the right time for any type of business organization to undergo a digital transformation. Big data is driving business and innovation, and understanding how to leverage the right data is key to gaining a competitive advantage. Organizations are adopting new technologies that help maximize the value of different types of data to gain insights that improve business intelligence, aid decision making, improve workflow and operational efficiencies, and the creation of new revenue streams.
With the prevalence of cloud environments and the Internet of Things (IoT) in conjunction with more affordable storage and processing, data storage is shifting from on-premise to off-premise data centers. Storing data in multiple data sources makes it more challenging for organizations to manage various types of data.
The Challenges of Data Management
The more applications an organization uses, the more that data becomes siloed and inaccessible. Migrating data from legacy data infrastructures and systems often contribute to the creation of data silos. Sharing data between multiple public clouds or between a public cloud and on-premise data center, or with a cloud data warehouse is particularly challenging. The average organization has a mix of structured and unstructured data in a variety of formats such as file systems, relational databases, and SaaS applications.
Data integration tools are used to compile different types of data from different sources, however, this makes agility difficult and organizations struggle to integrate, analyze, and share data and incorporate new data sources. The more sources of data an organization has, the more time data professionals spend on preparing and organizing data rather than on data analysis. Not only do organizations face quick data access roadblocks, but data quality is also an issue. Keeping different data types stored across multiple data sources increases the risks of errors in data.
What is data fabric?
The best way to approach data integration is to leverage data fabric. It’s an end-to-end data integration and data management solution that combines architecture, data management, and integration software, and shared data to help organizations manage data. A data fabric architecture results in a unified, consistent user experience and real-time data access. Big data fabric enables the flawless access and sharing of data in a distributed data environment.
Any organization with data assets can benefit from taking a holistic approach to data management. Business users need access to timely data, which is where data fabric comes in. A data fabric solution is ideal for geographically diverse organizations with multiple data sources and complex data use issues. Data fabric provides agility across operating and storage systems, scalability with minimal interference, and maximum data quality and data governance compliance while maintaining access to real-time data. A data fabric architecture is a distributed data architecture that comprises shared data assets and optimized data fabric pipelines.
What is a data lake?
A data lake is central storage that holds big data from a variety of data sources in a raw format. Data lakes contain a mix of structured, semi-structured, and unstructured data. Storing different data types in a flexible format makes accessing specific data easier in the future. Business users rely on identifiers and metadata tags to retrieve the right data from a data lake.
A data lake functions on the principle of schema-on-read, meaning there’s no defined schema that data needs to be fit into before storage. When a user requests data access, the data is parsed and adapted during processing into a needed schema. Not only does this save time defining a schema, but it also means data can be stored raw in any format. Data lakes make it faster and more accurate to access, prepare, and perform advanced analytics on data for a variety of use cases.
Accessing the true business value of big data requires the right data integration solution. The more diverse data sources and data types an organization has, the more important it is to access real-time, uncompromised data.