Our Five Key Points:

    • Big data architects plan and manage enterprise data strategies.
    • They may also be called data scientists and have a bachelor’s degree in data science or other related high-level qualification or years of experience in the data engineering field
    • For big data architects, ETL is one tool used to help integrate data into data warehousing solutions
    • Creating repositories of data that can provide business insights is a key responsibility of data engineers and architects.
    • Integrate.io offers a range of data integration solutions for the big data architect: ETL, ELT, CD, and more.

    The boom in Big Data has created an insatiable demand for data professionals at all levels. Analysts, DBAs, data engineers, security consultants – employers are crying out for people with the right skills and experience. Perhaps the most sought-after of all these professionals is the big data architect. We take a look at this role, and the various tools used by a big data architect: ETL (extract, transform, load), CDC, and other data integration and management solutions available via Integrate.io's platform.

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    Table of Contents

    1. What is a Big Data Architect?
    2. Why Does ETL Matter to a Big Data Architect?
    3. How Big Data Architects Use ETL
    4. Big Data Architects Needs Integrate.io

    What is a Big Data Architect?

    In the world of construction, architects are a bridge between clients and engineers. The client might have a sketch of their dream house, but the engineers can only start working when they have detailed blueprints. Architects take the client's sketch and create a functional blueprint for the house.

    Data architects work in exactly the same way. The architect sits with enterprise stakeholders who know what they want from their data but not how to achieve it, and asks questions like:

    •         What data sources are available?
    •         Who will use the data?
    •         When will they use the data?
    •         What kind of data processing will we perform?
    •         Which repository stores the data?

    When the requirements are clear, the architect then creates a blueprint that covers things like:

    •         Data entities and their relationships
    •         Data processing models, including pipelines between disparate systems
    •         Components required for processing data according to business needs

    Big data architects work the same way as relational data architects, except that they face a more complex set of problems. It's not just that the volume of data is bigger. Big data architects also have to create data strategies that account for requirements like:

    •         Handling unstructured data at scale
    •         Getting fast results from distributed file systems
    •         Working with innovative data repository structures
    •         Maintaining data quality and eliminating data swamps

    It's an extraordinary challenge, although you'll find it easier if you have strong data modeling and data science skills and the right tools. Data architects often have years of experience managing data strategies, using tools like Oracle, Hadoop, Azure, and managing complex data sets. They may also have high-level certifications and qualifications such as a bachelor’s degree in data science or computer science. Integrate.io’s expert team has a wealth of experience and provides numerous resources to support your data architect. Talk to us to find out more.

    Why Does ETL Matter to a Big Data Architect?

    For a big data architect, ETL (Extract, Transform, Load) is a foundational tool in data management. The ETL process, which first emerged in the 70s, involves three key steps:

    •         Extract: The ETL process pulls data from disparate sources, such as production databases and cloud services.
    •         Transform: Data passes through a transformation process. For example, ETL will transpose a relational database table into a different table structure.
    •         Load: Once data is in a standardized format, the ETL process loads it into a target repository, such as a data warehouse.

    Data architects now have access to sophisticated, cloud-based ETL platforms like Integrate.io that can move data in several ways. For a big data architect, ETL is just one of the many tools in their kit

    Integrate.io provides a fast, innovative ETL platform with a low-code environment, ideal for both big data architects and those with less technical expertise.

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    How Big Data Architects Use ETL

    Mention big data, and most people think of ELT (Extract, Load, Transform), which populates data lakes with unstructured data. While ELT works great in some situations,, there are several use cases where for the big data architect, ETL is the correct option.

    Data Pipelines

    Data strategy often comes down to a simple problem. What's the most efficient way to get data from A to B? The answer is generally some variation on ETL. You extract data, put it through an integration process, and deliver it to its destination.

    Modern cloud-based ETL solutions allow architects to build fully automated pipelines. These push data from source to destination via a staging database where transformations happen. 

    Another advantage of a cloud-based ETL is that they often come with a library of integrations. Integrate.io, for example, offers over 100 pre-built integrations with more added all the time. This means that the big data architect doesn't need to allocate resources for developing and testing hand-coded integrations. Instead, they can trust that their ETL solution will connect automatically to any supported services. 

    Data Lakehouse

    One drawback of big data architects using ETL is that it only supports structured data. Many data engineers are working with unstructured repositories such as data lakes. This is why some architects rely on ELT (Extract, Load, Transform) with on-demand transformation schemas. Integrate.io offers both ETL and ELT solutions, as well as real-time change data capture (CDC), making it ideal for various data formats.

    However, data lakes are not without drawbacks either. There is a processing overhead on queries, and some data lake platforms are effectively read-only. One compromise between these two structures is the data lakehouse: a data warehouse built on top of a data lake.

    The advantage of this approach is that you can use a fast-paced ELT process to populate your lake, and then you can fill individual data warehouses with cleansed and integrated data. The ETL process extracts directly from the lake, applies your required schema, and then loads it to the data warehouse. Integrate.io has more information on the data lakehouse here.

    Streaming Analytics

    Business data analytics solutions are currently worth over $67 billion, showing how important these insights are to enterprise leaders. From a data architecture perspective, you have to centralize all business-critical data, but you have to do it as quickly and efficiently as possible.

    Cloud ETL services can help. A platform-based ETL solution can act as a messaging service between the source database and the target repository, effectively allowing a push publication of data. For example, if an admin creates an order on the ERP, the order data immediately enters the data pipeline and ends up in a data repository. From there, it's a matter of giving business users access to the right business intelligence tools.

    Cloud Security

    The Big Data security market is worth $16.4 billion right now, with business forecasts estimating a rise to $43.82 billion by 2027. Data security is even more difficult in an age when there are few truly on-premise networks left in the world. Most organizations are either cloud-based or, more commonly, have a hybrid stack with cloud and on-premise components.

    Cloud ETL adds an extra layer of security when transferring data, no matter where it's from. The originating data source has a one-to-one connection with the ETL platform. This connection is modular, so a problem with one source won't affect any others. The ETL platform itself also has a one-to-one relationship with the data repository. 

    Talk to Integrate.io about our focus on data security and the most secure data transformation. 

    Metadata and Master Data Management

    Perhaps the biggest challenge for a Big Data architect is applying structure to unstructured data. How do you impose any order on a repository that's filling up with data from all enterprises?

    The answer is metadata and master data. Good architects will design a robust metadata policy. This creates consistency across the entire enterprise, making things easy to catalog and search. Master data management is another important strategy. This allows you to create a Single Version of Truth (SVOT) for data entities such as customers or products. You can then use the SVOT to validate the contents of your lake.

    The Big Data Architect ETL Solution: Integrate.io

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    Integrate.io is a new,cloud-based enterprise data integration platform with a variety of functions ideal for big data architects: ETL, ELT, super-fast CDC, and reverse ETL are all available via the platform’s intuitive interface. Empower your data management teams to create new data pipelines with ease thanks to a no-code environment. Gain faster insights from the apps and SaaS that are important to your business, and build them into your big data architecture with ease. The platform features numerous out-of-the-box pre-built connections, plus intelligent API creation and management making your data pipelines effectively limitless. Bring your SQL databases, Amazon AWS S3 buckets, Microsoft app data, and all your business SaaS insights together using one of the most innovative ETL tools on the market.  

    Schedule an intro call with our team and find out how Integrate.io can take your data strategy to the next level.