Alooma makes it easy to use your data warehouse of choice. Whether it's ad-hoc analysis, machine learning, data science, or operational analytics, Alooma lets you look past the pipeline for real insights, fast.
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Learn more about data warehousing
A data warehouse is a large repository of integrated data from one or many disparate sources. Data warehouses can contain historical or current data, typically for analytics and reporting. The data can come from operational systems like Salesforce or Marketo, from application SDKs or APIs, or even sensor data in the case of IoT. This data may require some cleansing, schema changes, or general formatting prior to use.
ETL stands for extract, transform, load and includes a process by which heterogeneous data is made homogeneous. The transform step typically adjusts data schema and format to work with the target data warehouse, prior to loading it. During this final load step, data is written to the target database or data warehouse.
Data warehouses — and the data they contain — typically embody the following four aspects: first, the data in them is subject-oriented — it helps you answer questions on subjects relating to your business. Your data warehouse contains data that was integrated from multiple sources, typically via an ETL tool. Next, a data warehouse is meant to help you analyze changes over time, so your data is time-variant. Finally, your data is nonvolatile — once it has entered your data warehouse, it should not change.
At Alooma, we've identified four primary use cases for modern cloud-based data warehouses. Ad-Hoc analysis involves creating business reports from disparate data sources, raw or in aggregate. Machine learning and data science uses statistical algorithms on large datasets to identify trends, discover hidden data relationships, and predict future events. Real-time and operational analytics involves monitoring business and team data by running continuous queries about various key performance indicators (KPIs). Finally, mixed-workload analytics requires some combination of the above use cases across an entire organization.
A data warehouse, especially with a modern ETL pipeline, lets you integrate, access, and analyze all of your data in real time. With these tools, you can gain meaningful insights into your KPIs, create sophisticated business reports, and use advanced machine learning algorithms to predict future events. And you can do it all with unparalleled speed, reliability, security, accuracy, and ease.
A cloud-based data warehouse is generally more reliable than an on-premise solution. The former may be maintained by the best data warehouse experts around, whereas the latter is only as good as your team. Cloud-based warehouses typically outshine their on-premise counterparts in terms of speed, reliability, security, and ease of use. Rather than continually fighting to reinvent the wheel, your users may immediately gain insights from their data, modernize their processes as new technology is developed, and even develop add-on functionality quickly and incrementally.