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5 Factors Data Scientists Should Consider In Their ETL Buying Decision

Blog - Advanced Analytics, Analytics, Big Data, Big Data Analytics

Let the truth be told: deciding on the right ETL tool for the organization is an intimidating task. With big data, the talk of the town and organizations grappling with high data influx and processing, choosing the right ETL tool becomes an important but daunting task. Clearly, the market is flooded with solutions that vendors claim can handle all of your big data needs. How can a data scientist cut through the marketing hype and determine the right solution for their enterprise needs?

Now, before we jump into considering the various aspects to decide the right ETL tool, let’s first understand in brief what exactly is an ETL tool and how does it make data preparation and discovery easier?

What is an ETL Tool?

As the acronym suggests, an ETL tool is a software built to extract, transform and load data, activities vital to data warehousing projects. These tools come with pre-configured components that automate data wrangling steps, processing it through various layers of cleansing and augmentation, resulting in data that is ready for analysis. By using a powerful and carefully chosen ETL tool, organizations can bring big data to bare effectively.

The ABCs to ETL

In a nutshell:

  • Data is sourced from any number of systems, in any format. Each source might house the data in a different format, such as relational tables (RDBMS), columnar stores, text files, video, and the like. The best ETL tools can process data in all of these forms:
    • Structured: well-defined schema with rows and columns, e.g. RDBMS.
    • Unstructured: no pre-defined schema, can be textual or non-textual, human or machine-generated. Data may also be stored within a columnar store (e.g. NoSQL). Examples of data commonly found here include image, audio, and video files.
    • Semi-structured: A form of structured data that is typically not in row. column format, such as JSON and XML.
  • Data is moved through any number of processing steps – transformations – by applying various functions to arrive at the required end state. This phase has many sub-stages where data can be further processed by trimming, appending, filtering, aggregating, etc.
  • The final stage – loading – is where transformed data is populated into the target repository, such as a relational database, Hadoop, or even extracted as files.
Vendor management

Thus explained, let’s consider the important factors when deciding which ETL tool is best for your data science needs.

Connectivity and Data Integration. The tool should easily connect to different data sources to fetch the data that is needed. It should have the ability for data cleansing using metadata approaches. Many ETL tools can handle only structured data from sources, thus the challenge arises when you encounter data that is semi-structured or unstructured. Data scientists should be clear about what their enterprise needs are and make informed decisions based on the capability of the software.

Scalability. Data Scientists should consider if the ETL tool can grow with your enterprise needs. That is, will the tool and embedded code scale to sizes 2x, 5x, 10x or more of your current demand? Does your ETL tool offer native connectivity to a broad range of data sources? These factors will help determine how (and if) your data platform can evolve with technology advancements.

Ease of use. It’s important to ensure that your ETL tool can be easily installed and maintained in-house. Data engineers should find it easy to understand and learn, and carry out ETL processes smoothly. To ascertain this, completing a proof-of-concept is advisable (a reputable vendor will provide a cost-free license for this evaluation purpose). Testing the software in your own environment will give an idea of the functionality, the extent of usability, and the performance of the tool. Be sure to have your source files accessible beforehand and be clear what results you wish to achieve.

Metadata support. This is a key feature of an enterprise ETL tool. While almost every ETL tool supports metadata capturing and maintenance features, the main challenge arises when sharing the metadata at different segments of an information management system. Enterprises should be clear on metadata management before they purchase an ETL tool as metadata capabilities enhance the speed and quality of integration.

Performing Data Science Functions. ETL tools should allow for embedded scientific methods, algorithms, and processes that perform statistical transformational functions and visualizations on data at rest or in motion. Moreover, the ETL tool should provide access to built-in machine learning models that can predict, score, or otherwise inform the data scientist as to the nature of the data.

Does such an ETL tool exist?

Glad you asked!  Fulfilling all these above criteria and more, HCube™ takes ETL to the next level.

  • Hcube™ has a comprehensive ability to natively connect to different sources of data, including Hadoop, SQL, NoSQL, and flat files as sources or targets.
  • HCube™ supports extensive data transformations using drag-and-drop.
  • HCube™ can process data in batch, streaming, or queueing modes.
  • HCube™ can build, train and validate predictive models right within your workflow.
  • HCube™ provides leading visualization capabilities using Microsoft PowerBI.

Still struggling to decide on the right ETL tool?  Let our experts assist you with the process. (call to action button)

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