Evolving Your Data Management Strategy Beyond Data Warehousing
Data is much more alive and dynamic than ever before. It’s not just information: It’s action. Data feeds machine learning (ML) and artificial intelligence (AI) algorithms, connects workflows, and provides meaningful insights. The “new frontier” of data ingestion goes beyond warehousing to enable a lot more choice. Opting for an open-standard system, in which data is independent from the software used to analyze it, allows companies to leverage a range of different tools while maintaining high performance.
Most data starts out in online transactional processing (OLTP) systems supported by companies like Oracle and IBM. They’re experts in managing high volumes of transactions, but these systems weren’t designed to help companies leverage trends. To address this challenge, companies like Confluent and Azure Synapse set up Apache Kafka cloud services that allow any company producing Kafka data to connect and move data from relational sources to cloud and non-relational platforms such as Salesforce, Google Cloud, Snowflake, Databricks, etc.
The process of evolving data management strategy is not about scrapping legacy technologies. It’s about leveraging open standard technology to establish a network of data pipelines. Perhaps, for example, data starts with an Oracle source system piping through SQDR. SQDR might move change data to Confluent using Kafka messaging; then, Confluent could pipe data into Salesforce or elsewhere. In this case, data moves from Oracle to Salesforce using several layers of technology. Dynamic data movement relies on open standard technology to go beyond writing SQL queries to find data points in traditional data warehouses.
Cloud technologies enable choice. Some companies prefer to stream data into cloud-based delta lakes while maintaining their existing data warehouse; that way, they can take advantage of new technologies from companies like Synapse while maintaining their existing applications. Others would prefer to get rid of their in-house data center all together.
StarQuest encourages customers to make improvements by integrating technologies that allow them to use their data better. We bring the robustness and power our customers have come to expect from data warehousing to delta lakes, too. Advancing data management strategy is not about displacing current software and hardware investments; it’s about making it easier to leverage new technologies that can unlock your data’s embedded potential.
For more information on SQDR and our delta lakes integration, contact us.