Data Warehouse, Data Lake & Data Hub
Given the increasing magnitude of distributive frameworks for data processing, organizations encounter colossal amounts of data spread across contrasting and unconnected systems. Moreover, since the endorsement of Software-as-a-Service (SaaS) applications and cloud services, with larger data and varied access patterns, business operations and IT are facing an ongoing discord over the need to share data. Data silos are the result of the increasing challenges of unifying data.
Adopting DataOps for Agile Data Management Processes
As businesses become AI-ready, efficient data management has acquired an unprecedented role in ensuring their success. Bottlenecks in the data pipeline can cause massive revenue loss while having a negative impact on reputation and brand value. Consequently, there’s a growing need for agility and resilience in data preparation, analysis, and implementation.
The World of Data, Warehouse, Lake and Hub
Every organization has enormous amounts of data that needs to be stored and managed. Effective data management is the critical aspect of any organization and the ability to access and communicate the data are the key factors that decide the efficiency in data management. The sheer volume of data that must be managed by organizations has increased so markedly that it is often overwhelming to manage it.
Cloud or Data Center — Where’s the Best Place for Your Data?
Clients often ask us questions about whether it’s better to have a data center for a company’s digital assets or be in the cloud. Because a dedicated data center doesn’t have to be at a client location, it is easy to get confused about these concepts.
Is a Data Lake Correct for You?
The late 2000s saw an exponential growth in the number of people owning a computing device along with some form of connectivity to the internet. As we reached 2017, this resulted in enormous amounts of (often) heterogeneous & unstructured data being generated on a daily basis. “Big Data” thus became the norm and traditional Data Warehouses became increasingly unable to accommodate this change.
What are Plural Data Warehouses?
The term “Data Warehouse” intuitively brings to mind a unified representation of data across an enterprise. While this is the standard theoretical look of a data warehouse, many practical scenarios demand that we take alternate approaches as well. It is thus not uncommon for an organization to face scenarios where multiple data warehouses are implemented to handle data across the enterprise. Although it may be misunderstood as an indicator of ineffective data organization, this concept of using multiple warehouses (also known as the plural data warehouse concept) is often a simple indicator of an evolving firm.