Big data vs data warehouse LEARNOVITA

Big Data vs Data Warehouse | Know Their Differences and Which Should You Learn?

Last updated on 04th Nov 2022, Artciles, Blog

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Karthika (Data Engineer )

Karthika has a wealth of experience in cloud computing, BI, Perl, Salesforce, Microstrategy, and Cobit. Moreover, she has over 9 years of experience in Data Engineer in AI can automate many of the tasks that data scientists and data engineers perform.

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    • In this article you will learn:
    • 1.Introduction.
    • 2.Drinking from a firehose with a big data system.
    • 3.Using data warehouses for reporting infrastructure.
    • 4.Comparing big data to data warehouses.
    • 5.Is big data better than a data warehouse?
    • 6.Conclusion.


If terms like a “database” or “data warehouse” refer to the overflowing cup of data, “big data” is the description of the liquid itself. Although these are often compared to directly as in this article it is important to remember that there is categorical difference between the big data and a data warehouse. The first is a set of a doctrines or a toolbox for the dealing with very large volumes of data; the second is single tool .

Drinking from a firehose with a big data system:

Big data an overflowing is often explained as being composed of a three Vs: volume, velocity, and variety. Once these parameters reach certain magnitude traditional database tooling can no longer service of flow and a big data system becomes necessary alternative for users. Big data systems are the typically designed to handle big values of any or all of these are three parameters by implementing a following set of core features:

Distributed processing and storage: A big data system will spread the physical storage across the several networked locations. This lets it accommodate an arbitrarily large loads on its a data system, reduce strain on a network systems, and enable highly parallel queries and processing. Most commercial services provide a resources from their own stock, but an especially large systems might include in-house data centers.

Data stored without complex structure: Unlike a relatively low-scale, highly specific data storage big data imposes are no rigorous schemas or normalization. This might lead to be a messier data, but messy data is precisely what are big data system is meant to handle. The efficiency gained by aneglecting storage structure pays off at a scale.

Arbitrary data types handled without a complaint: Big data is a famously designed to be indifferent to types of a data. This agnostic storage attitude makes the scaling with new technologies or an expanded product demands more simpler. A big data system stores the any data type.

Indefinite scaling on above criteria: Although there are be structural and cost-based tradeoffs to ensure the above criteria are met, a big data system is be worth it so long as it can a scales up. An Elastic responses to increased the volume, velocity, or variety of a data are necessary for the any big data system. If there is scale limit to a system it is essentially failing.

Using a data warehouses for reporting infrastructure:

  • This article compares the big data to data warehouse, a data system designed to make the analysis, modeling, and visualization of a varied databases more performant.
  • A data warehouse is the data structure that synthesizes different data sources into the central data system. The warehouse coordinates various types of a data series, usually by a timestamp, allowing correlational analysis over complex historical relationships.
  • It is intended to the facilitate large and complex query operations on the wide range of a data — but ultimately a warehouse is the single data system hosted in cloud or on-premises.
  • A Redundancies and distribution might be an implemented, but in many regards of warehouse expands the functional role of relational database: a limited, single structure that a retools rigid schemas into the usable information with more responsiveness.
Data Warehouse

Comparing big data to a data warehouses:

One of problems with the comparing big data to data warehouses is that a two tools address various problems. Their goals are essentially identical — take in large amount of data and convert it into the profitable insights. However, a “large amount” of data handled by a big data system is well beyond anything data warehouse could reasonably be consume. Big data must account for a network and application latencies, implement backups, and a sustain distributed networks that are some more complex than what is needed to implement an adequate data warehouse.

    ComparisonData WarehouseBig Data
    Data ecosystem A Company-wide data is used to increase an internal transparency or generate data insights. A unique, large-scale solution addresses are otherwise impractically large data sets.
    Data inputs Data comes from a one or many relational databases: well organized but potentially diverse datasets. Data can be a complex, but individual relational databases feeding a warehouse have structure. Data inputs are an arbitrary; anything goes. Sources are often users, automatic logging, or other data generation that creates a data very quickly. Data does not need any structure.
    Formats Most data warehouses are mainly consume a structured data from relational databases. Any input formats are can be acceptable.
    Time Series Data warehouses are an explicitly built to coordinate a different data series to be single time axis, putting complex data into the common context. Big data technologies sacrifice well-kept time data to a support data from the any source. They can struggle to the contextualize data over synchronized time series.
    Memory Data created or stored by a warehouse is not overwritten, even if the underlying databases are be modified. Data warehouses are therefore “non-volatile” storage systems. Big data systems do not wipe old data and instead use elastic large-scale storage to keep previous data even without time tags.
    Processing Data warehouses are primed for more responsiveness to the small volume of queries, employing an ETL tools to optimize aggregation over a time series. To handle wide demand on a data input/output, big data systems use a Hadoop or similar MapReduce algorithms to quickly serve variety of write or read operations.
Big Data

Is big data better than data warehouse?

  • Sure in all the sorts of situations! Most of a time though it is not clear whether can better utilize a big data or a data warehouse. More companies find themselves in situation where the both approaches to data analysis are useful.
  • If find asking whether a big data is better than data warehouse, certainly have some data that need to convert into an actionable information. To decide which option is a better, break down a type and quantity of data are have to work with consider a user base for any analysis tool and the resources have to apply to problem. In general, larger and more diverse a data sources, audience, and resources, the more appropriate a big data solution will be to use case.
  • Nevertheless in more contexts that are satisfied with the big data approach, data warehouses are still a valuable tool for a smaller subsets of analysis. Perhaps the financial team needs to model a highly time-dependent outcomes from fixed scope of well-structured data they don’t need a wide range of messy data in existing big data structure. Or maybe a marketing team needs to have a tight well-contained system to visualize can up-to-date company data for an investor pitches they don’t need to interface with a complex a big data system.


A data warehouse can only manage structured data (relational or not) but big data can handle structured, non-structured, and semi-structured data. Big data typically uses a distributed file system to load large amounts of data in a distributed manner while a data warehouse does not have such idea.

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