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- Data Analytics vs. Data Analysis
- Quantitative vs. Qualitative Data
- Data Analytics vs. Business Analytics
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- The Difference Between Data Analytics and Statistics
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- Data Analytics Lifecycle
- Data Analytics vs Business Intelligence
- What is Behavioral Analytics?
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- What is Data Analytics?
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- A top-level guide to data lakes
- Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
- Data Warehouse Architecture
- What Is a Data Warehouse?
- How to Create and Use Business Intelligence with a Data Warehouse
- Best Practices for Accessing Your Data Warehouse
- Data Warehouse Best Practices — preparing your data for peak performance
- How do Data Warehouses Enhance Data Mining?
- Data Warehouses versus Databases: What’s the Difference?
- What are the Benefits of a Data Warehouse?
- Key Concepts of a Data Warehouse
- Data Warehouses versus Data Lakes
- Data Warehouses versus Data Marts
- Difference Between Big Data and Data Warehouses
- How to Move Data in Data Warehouses
- What Is Customer Data?
- Customer Data Analytics
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- Collecting Customer Data
- The Importance of First-Party Customer Data After iOS Updates
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- A complete guide to first-party customer data
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- How To Handle Your Company’s Sensitive Data
- Data Security Best Practices For Companies
- What is Persistent Data?
- Google Analytics 4 and eCommerce Tracking
- What Is Google Analytics 4 and Why Should You Migrate?
- GA4 Migration Guide
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- What are the New Features of Google Analytics 4 (GA4)?
- Benefits and Limitations of Google Analytics 4 (GA4)
- Understanding Google Analytics 4 Organization Hierarchy
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Difference Between Big Data and Data Warehouses
There is a great deal of data in the world, and there's always more data being produced. The concern about the quantity of data generally becomes the question of what to do with the data. Our cups overflow, so we have to research larger, better cups on the Internet.
If terms like “database” or “data warehouse” refer to an overflowing cup of data, “big data” is a description of the liquid itself. Although these are often compared directly, as in this article, it is important to remember that there is a categorical difference between big data and a data warehouse. The first is a set of doctrines or a toolbox for dealing with very large volumes of data; the second is a single tool (often employed in that toolbox). Nevertheless, as we will see in this article, the two often fill similar roles, consuming similarly diverse datasets and producing reports or analysis on that data.
Drinking from the firehose with a big data system
Big data — the overflowing content — is often described as being composed of three Vs: volume, velocity, and variety. Once these parameters reach a certain magnitude, traditional database tooling can no longer service the flow, and a big data system becomes a necessary alternative for users. Big data systems are typically designed to handle large values of any or all of these three parameters, by implementing the following set of core features:
- Distributed processing and storage: A big data system will spread physical storage across several networked locations. This lets it accommodate arbitrarily large loads on its data system, reduce strain on network systems, and enable highly parallel queries and processing. Most commercial services offer resources from their own stock, but especially large systems might include in-house data centers.
- Data stored without complex structure: Unlike relatively low-scale, highly specific data storage tools (such as relational databases), big data imposes no rigorous schemas or normalization. This might lead to messier data, but messy data is precisely what a big data system is meant to handle. The efficiency gained by neglecting storage structure pays off at scale.
- Arbitrary data types handled without complaint: Big data is famously designed to be indifferent to types of data. This agnostic storage attitude makes scaling with new technologies or expanded product demands much simpler. A big data system stores any data type.
- Indefinite scaling on the above criteria: Although there are structural and cost-based tradeoffs to ensure the above criteria are met, a big data system is worth it so long as it scales up. Elastic responses to increased volume, velocity, or variety of data are necessary for any big data system. If there is a scale limit to the system it is essentially failing.
Using data warehouses for reporting infrastructure
This article compares big data to the data warehouse, a data system designed to make analysis, modeling, and visualization of varied databases more performant.
A data warehouse is a data structure that synthesizes various data sources (usually relational databases) into a central data system. The warehouse coordinates different types of data series, usually by timestamp, allowing correlational analysis over complex historical relationships. It is intended to facilitate large and complex query operations on a wide range of data — but ultimately a warehouse is a single data system hosted in the cloud or on-premises. Redundancies and distribution might be implemented, but in many regards the warehouse expands the functional role of a relational database: a limited, single structure that retools rigid schemas into usable information with high responsiveness.
Check out our page Key Concepts of a Data Warehouse to dive deeper into the fundamentals of data warehouses.
Comparing big data to data warehouses
One of the problems with comparing big data to data warehouses is that the two tools address different problems. Their goals are essentially identical — take in a large amount of data and convert it into profitable insights. However, the “large amount” of data handled by a big data system is well beyond anything a data warehouse could reasonably consume. Big data must account for network and application latencies, implement backups, and sustain distributed networks that are far more complex than what is required to implement an adequate data warehouse.
With these caveats in mind, let’s compare the two data systems, keeping a special eye on the difference in scope that can determine which tool is appropriate for the job.
|Comparison||Data Warehouse||Big Data|
|Data ecosystem||Company-wide data is used to increase internal transparency or generate data insights.||A unique, large-scale solution addresses otherwise impractically large data sets.|
|Data inputs||Data comes from one or many relational databases: well organized but potentially diverse datasets. Data can be complex, but the individual relational databases feeding a warehouse have structure.||Data inputs are arbitrary; anything goes. Sources are often users, automatic logging, or other data generation that creates data very quickly. Data does not require any structure.|
|Formats||Most data warehouses mainly consume structured data from relational databases.||Any input formats are acceptable.|
|Time Series||Data warehouses are explicitly built to coordinate different data series to a single time axis, putting complex data into a common context.||Big data technologies sacrifice well-kept time data to support data from any source. They can struggle to contextualize data over synchronized time series.|
|Memory||Data created or stored by the warehouse is not overwritten, even if the underlying databases are modified. Data warehouses are therefore “non-volatile” storage systems.||Big data systems also do not overwrite old data, employing elastic large-scale storage to retain historical data even without time tags.|
|Processing||Data warehouses are primed for high responsiveness to a small volume of queries, employing ETL tools to optimize aggregation over time series.||To handle a wide demand on data input/output, big data systems use Hadoop or similar MapReduce algorithms to quickly serve a variety of write or read operations.|
Is big data better than a data warehouse?
Sure, in all sorts of situations! Most of the time, though, it is not clear whether you can better utilize big data or a data warehouse. Many companies find themselves in a situation where both approaches to data analysis are useful.
If you find yourself asking whether big data is better than a data warehouse, you certainly have some data that you want to convert into actionable information. To decide which option is better, break down the type and quantity of data you have to work with, consider the user base for any analysis tool, and the resources you have to apply to the problem. In general, the larger and more diverse your data sources, audience, and resources, the more appropriate a big data solution will be to your use case.
Nevertheless, in many contexts that are satisfied with a big data approach, data warehouses are still a valuable tool for smaller subsets of analysis. Perhaps a financial team needs to model highly time-dependent outcomes from a fixed scope of well-structured data; they don’t need the wide range of messy data in an existing big data structure. Or maybe a marketing team wants to have a tight, well-contained system to visualize up-to-date company data for investor pitches; they don’t want to interface with a complex big data system.
In any situation, it is necessary to consider the specific use case and deploy the most appropriate tool. Using big data systems is like fishing with a drag net. You will quickly accumulate vast amounts of “things” from the water. Not all of it will be fish and certainly not just a specific kind of fish. The drag net will capture anything it comes across, including garbage and other debris. A data warehouse is like a fishing supply store. It will give you the right kind of rod, lure, and bait and will direct you to the specific body of water that has the fish you are looking for.
A tool (or technology) for any job
Sometimes you may hear big data referred to as a “technology” — implying that it is complex and composed of many parts. This is distinct from a data warehouse, which is a single system tailored to handle a particular problem. Although they fill similar roles, we have seen how there is a categorical difference between big data’s large-scale use case and a data warehouse’s specific historical view of useful data, and seen some examples of when each approach is useful.
Advancing big data technology may eclipse the role of a data warehouse. Perhaps your data needs will one day be met by an entirely new approach. To remain competitive in a world that relies more on data analysis every day, you must stay abreast of the latest developments. That’s why our learning center offers up-to-date information about a variety of other data systems.
Check out other articles that contextualize the role of a data warehouse:
- How do Data Warehouses Enhance Data Mining?
- Data Warehouses versus Data Marts
- Data Warehouses versus Data Lakes
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