SWITCHBOARD ETL PLATFORM

Unify data at lightning speed and unlock the analytics your business needs

Implement the only data automation solution that creates agility

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Extract & Load

  • Instantly connect to any data source without writing a single line of code

  • Quickly create custom connectors with our world class experts

Transform

  • Convert data to the schema that your team requires
     

  • Incorporate business logic that’s unique to your business
     

  • Generate foundational data that you can rely on
     

Visualize & Analyze

  • Sync your data with leading business intelligence tools
     

  • Deliver reports and analytics to the business teams
     

  • Get the clearest possible view of your opportunities
     

Extract and Load

  • Instantly connect to any data source without writing a single line of code.
     

  • Take advantage of our extensive list of pre-built connectors.
     

  • Quickly create custom connectors with our world class experts.

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Implement a Smart ETL Platform
 

Easily create and manage multiple API calls into multiple sources.  Extract only the specific, granular data you require for use.  Only pull the data you need when you need it.

How Switchboard’s ETL platform works

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Drive Real-Time Analytics
 

Automate with a comprehensive SaaS platform combined with world-class, dedicated customer success engineering. Non-technical business teams get to the desired dashboards, when they need it, regardless of how many data pipelines you have or how many ways you need to cut the data. Be free to focus on analytics and insights.

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Ensure Security and Compliance
 

Rely on best in class controls, security and compliance practices with Switchboard, a SOC-1 and SOC-2 certified service company. Trust a single source of truth for your data with the utmost confidence.

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Switchboard enabled us to get our first data sets onboarded before new engineering resources were able to be brought in, so they could hit the ground running. It’s that kind of velocity and agility that has enabled us to take advantage of the data opportunity so quickly.

Ameen Kazerouni, Chief Data & Analytics Officer

Orangetheory Fitness

ETL: the ultimate guide

 

ETL is a critical process for any data-driven enterprise. Switchboard automates this process and adds proprietary components to make the output ideal for analytics. If you’re wondering: “what does ETL actually mean?” when it comes to applying it to your data, then read on.

 

What is ETL and how does it work?

 

In its simplest sense, ‘ETL’ (Extract, Transform, and Load) is a term used to describe the overall process of extracting raw data from multiple disparate sources, transforming that raw data into foundational data, and loading it into a data warehouse. 

 

Sounds easy, right? Implementing ETL is anything but simple. When revenue and operations executives talk about data unification, we often hear the response, ‘Oh, that’s just ETL’. But what exactly is ‘ETL’? And why shouldn’t you dismiss it as a trivial process? Why is it so hard to perform manually and when should you look to automate? Let’s dive into some of the complexity involved to answer all these questions and more.


What is ‘extract’ in ETL?

 

‘Extract’ refers to the process by which the raw dataset is copied or exported from its original locations into a temporary staging repository, before it can be moved to its final destination. Extraction must be processed correctly so that data can be successfully passed to the subsequent phases. 

 

Data unification almost always involves multiple sources. Moreover, the data can be structured or unstructured. Structured datasets comprise well-defined types and patterns that make them easy for computers to parse, such as an SQL relational database, whereas unstructured datasets are normally classified as ‘everything else’, such data from audio, video and social media content.

 

There are many factors to consider during the extraction phase, and each comes with a plethora of questions which need to be answered:

 

  • Timing is important – When do you extract the data? What timezone are the timestamps in the dataset? 

  • Sometimes a source fails or is missing – Do you then re-pull data from previous days and backfill? What do you backfill and how? How often do you pull the data?

  • Sometimes extraction involves API calls to a data source - Do these have a rate limit? Does the API have a  quota? What happens if the API is down? Do you know how the API works? Who maintains the code used to connect to the API? Do you have engineers who know how to manage sudden or unexpected API changes?

  • Security and regulatory compliance: What credentials are required to extract the data? Data extraction needs to be carried out securely - how is this security maintained? Who is responsible? 

  • The scale of the data – How large are the datasets? Do you have the resources to extract that much, in terms of compute, storage, and scale, but also time and engineering resources? What happens when they grow in variety or volume?

  • All of these factors affect cost – Have you budgeted enough for the engineering resources involved in extracting all your data? Are you extracting too much data, leading to unnecessary warehousing costs?

 

All these questions – and many more – must be answered comprehensively for extraction to be successful.
 

 

What is ‘transform’ in ETL?

 

During this phase, the raw data is transformed into foundational data. A series of rules are applied to the extracted dataset to aggregate and normalize it for its intended use. This involves several overarching processes, each with their own set of sub-processes:

 

  • Data cleansing – Inconsistencies and missing values in the raw data must be resolved. This involves filtering, deduplication, validation, and authentication.

  • Standardization – Formatting is applied to the data. This involves calculation, translation, and summarization, such as unit conversions and tallies.

  • Verification – Unusable data and anomalies need to be flagged and removed. This involves auditing the dataset to ensure it is of sufficient quality, and that it is compliant with its use-case.

  • Formatting and sorting – The data must be configured to match the schema of the intended target location. This requires sorting the dataset according to type, and placing it in tables or databases of the correct format.

  • Labeling – during transformation, files should be labeled correctly and consistently to ensure the data can be loaded and used properly.

  • Governance and protection – Any dataset which is subject to regulation may need to be encrypted or removed.

 

As with extraction, each step of the transformation phase will throw up a number of questions and complications. What are you transforming? Where are you transforming? How do you know the rules you are applying are correct? How can these rules be audited? Do you have the processing power to apply all these rules? What’s the processing cost? When the extraction process changes during transformation, such as Facebook adding a new data point, how do you manage that change? This would affect scheduling. Do you return to the extraction phase to backfill and fix it? How do you track the changes made to your processes? 

 

The scale of the dataset will also affect a number of factors: How do you process a large dataset, and how fast will it be? Who oversees validation? For example, this could be an engineer or a data analyst. This is not an exhaustive list of considerations, but a sample of the questions which need answering in the transformation phase.
 

 

What is ‘load’ in ETL?

 

The final phase of ETL involves loading the transformed data into the target destination, which is usually a data warehouse or data lake. Typically, a ‘full loading’ is implemented initially, which includes all data, followed by a periodic ‘incremental loading’ of any changes. During incremental loading, the incoming dataset is compared with existing records to determine whether unique information is available. If so, then data points are overwritten or new records are created.

 

While ‘load’ may seem like the simplest phase, there are also a myriad of questions to consider here. 

 

  • The properties of the target destination – Where are you putting the data, and who is responsible for maintenance? Is the dataset correctly cataloged? 

  • Support for the business team – Does the business team, who rely on the dataset, know how to use it? What happens if some of the data fails to load, or is malformed? If a new source is needed, who does the business team need to contact? For example, if they find they want to integrate Snapchat data, how do they request this? Do they need to go back to the team taking care of the extraction phase?

  • Maintenance cost – Data must remain valid. Who is responsible for this and how much resource will it take?

  • Archiving – What about archiving old data? Do you have a naming convention? How do you know if these processes aren’t running, and who’s monitoring it? 

  • Data governance – Is the dataset under any form of governance which requires certain rules to be followed? Is there any PII (Personally Identifiable Information)? How do you audit and regulate the data?

 

It’s relatively straightforward and inexpensive to activate data warehouses. But all of these factors – and the scale of the data involved – mean data storage can quickly become expensive and time-consuming to maintain.
 

 

What are ETL tools?

 

Broadly speaking, ETL tools are software platforms which help facilitate ETL processes, and there are several different types. Enterprise ETL tools are commercially supported, but are often expensive. Open source tools are free and the source code can be modified according to your needs, but they come with no support.

 

There are also cloud-based ETL tools, which leverage cloud technology to provide greater availability and elasticity. However, these platforms usually do not support datasets stored in other locations, so they must first be transferred to the provider’s platform. Some companies even develop their own custom ETL tools, the advantage being that the resulting tool is flexible and bespoke to their needs. The disadvantage is that developing your own tool requires considerable internal resources, including testing, maintenance, and updates.

 

The top ten attributes of efficient ETL tools

 

The complex nature of ETL means there is no single tool which is perfect. But by automating the process as far as possible, you can start to realize the benefits of implementing ETL efficiently at scale. Here are ten of the top features you should be looking for in an ETL tool. 

 

  1. Excellent credential management – Different extraction sources require different credentials to access their respective data, so these will need to be accessible by the system, secured, and easily manageable.

  2. Comprehensive integrations – The best ETL tool will be compatible with every API you require since it’s useless if it can’t process all of the required data. The ideal tool would support a comprehensive number of data connectors that move data between databases, and can process data in a wide variety of file formats.

  3. Maximal automation & scheduling – The whole reason you’re using ETL is to avoid manual inputting. Therefore, automating as many of the integrations as possible will save time. And since ETL can involve hundreds of integration jobs each day, the workload can scale up rapidly. The interdependence of jobs also means that scheduling control is key to orchestrating a pipeline with complex sub-processes.

  4. High-performance quality – The quality of any piece of software comprises three main elements: accuracy, stability, and speed. Accuracy is the minimization of errors, stability is how consistent the tool is without crashing, and speed is simply how fast it performs operations. Good parallel processing is an important characteristic, since this will allow different operations to run simultaneously on multiple nodes. Pushdown optimization is another major feature, which allows data transformation jobs to be moved into a relational database when needed. This makes better use of resources and enhances performance further.

  5. Both on-premises and cloud-based – Ideally, you would have the option to deploy your ETL tool either on your own servers or in the cloud. You should also be able to efficiently containerize the software, virtualizing the application and the operating system upon which it runs. This enables you to deploy the tool on different platforms while minimizing the resources required. Containerization also aids with patching and scaling of the tool.

  6. Built-in data profiling – Before running ETL, you’ll need to examine the source dataset carefully to determine its structure, quality, and integrity. The best tool will enable you to look at this easily and in detail. It should also support custom data quality rules so that the system can automatically identify missing or invalid records.

  7. Data governance – This is the collection of processes and policies which ensure the quality and security of the data used by an organization. Through consistent and common processes and responsibilities, it ensures the most effective and efficient use of information to achieve the ETL objective. Therefore, your software must support your data governance standards, and should also be compliant with legal regulations, such as GDPR and CCPA.

  8. Security – If your datasets contain sensitive information or PII, this must be encrypted, with access gated appropriately. A typical problem in ETL is that the extracted dataset remains in a data store until it is transformed, leaving it vulnerable to unauthorized access. Data security is therefore paramount to protect your organization from data breaches, regulatory fines, and loss of client trust.

  9. Monitoring & alerting – When large datasets are involved, ETL requires a number of different data pipelines. These may fail during processing for a number of reasons, such as network disruption. Therefore, an ETL tool needs to monitor activity and alert you when there’s a failure in real-time.

  10. Ease of use – As with any good software, an intuitive interface and easy operation are desirable. Ideally, your ETL tool would simplify functions, feature a drag-and-drop GUI, and avoid dealing directly with SQL, so that non-developers find it user-friendly.
     

 

What is causing the demand boom for ETL tools?

 

Simply put, it’s the mushrooming growth in data. The total amount of data worldwide was estimated to be 64.2 zettabytes in 2020, and is expected to reach 180 zettabytes by 2025. As internet use expands, the number of online users will increase, as well as the number of different internet services used by each user. The vast amount of data that’s generated daily has resulted in a rising need for transfer and unification, or in other words, ETL. Enterprises are therefore looking for ways to automate ETL, so as to make it easier and faster to deal with increasingly large and disparate datasets.

 

The five steps of the ETL process

 

ETL is usually referred to in three stages, but in a real-world data unification scenario there are at least five (‘ECTLA’):

 

  1. Extract – Capture data from disparate sources into a temporary staging location.

  2. Clean – Ensure the quality of the data prior to transformation through a series of standardization and normalization steps, removing any defects and invalid records.

  3. Transform – Process the dataset by applying a series of rules, then convert it to the correct format for the intended target destination.

  4. Load – Transfer the data to the target destination, which is usually a data warehouse.

  5. Analyze – Evaluate the finished data to gain insights for business uses.
     

Data integration

 

Data integration is the process of combining datasets from different sources to provide a unified view of them. This is part of the ‘extract’ phase in ETL.

 

ETL testing

 

ETL testing is the process of validating the output dataset from the ETL process to ensure it has been extracted, cleaned, transformed, and loaded correctly. It is also used to measure the performance of the ETL procedure itself to identify issues such as processing bottlenecks, substandard data quality, or record mismatches.

 

ETL testing typically includes the following:

 

  • Assessment of data sources – For example, a count of the records contained in the source data so that you can confirm everything was successfully transferred.

  • Test cases – Evaluating the integrity of the data on the target system, and the performance of the ETL processes.

  • Documentation of issues – Noting any bugs or errors which arose during the ETL procedure.

 

Example of an ETL pipeline

 

It’s easier to understand ETL by looking at an example. We’re going to use YouTube as our pedagogical data pipeline.
 

 

Steps to building a YouTube data pipeline

 

In this example, we’re going to assume that you want to combine analytics data from different YouTube channels. Here are the steps required to build the data pipeline as they pertain to ETL:

 

Extract:

  1. Obtain the necessary credentials for YouTube Channel Reports.

  2. Access all of the video information from each channel.

  3. Use this video information to access metrics, such as views and ad revenue.

 

Transform:

  1. Join your video metadata to your metrics.

  2. Standardize the data, such as adjusting time zones and converting currencies.

  3. Merge the data into a final report.

 

Load:

  1. Update the data warehouse with the report from the transform phase.

  2. Update the data warehouse with the video metadata.

  3. Verify that the dataset has been loaded correctly.
     

 

How to build an ETL pipeline

 

How you build an ETL pipeline really depends on the tools you’re using. The two programming languages most commonly used for ETL are SQL and Python.


 

Is a data pipeline the same as ETL?

 

A data pipeline usually refers to a production-ready procedure that has proven to be reliable and secure, so can be used repeatedly. In contrast, ETL is a unique and bespoke event which produces a major change in the nature of the data. While data pipelines involve transferring datasets, they do not necessarily transform them. An analogy would be to compare a delivery firm with a moving company: A data pipeline is like a global courier with repeatable processes, monitoring, and scale, whereas ETL is like a moving truck used for a one-off transportation between homes.


 

Building an ETL pipeline in SQL

 

While SQL is certainly a tool used in ETL, it is simply a query language, i.e., a special language used only for searching and updating the existing tables in a data warehouse. However, SQL lacks the functionality to access datasets from disparate systems, so you must first transfer the dataset into a warehouse to take advantage of them. In contrast, ETL describes the whole process of extracting, transferring and loading your data. Therefore, SQL is a language that can be used as part of an ETL procedure, but it is not the procedure itself.
 

 

Building an ETL pipeline in Python

 

Python is a versatile language that can be used for many different applications, including building ETL pipelines. However, similarly to SQL, it is also not the ETL pipeline itself. To build an ETL pipeline in Python, you need many components, including engineers, deployment, version control, and security patches. You also need to manage these factors effectively to ensure they work in harmony – otherwise your pipeline will break.
 

 

Example of a Python ETL pipeline

 

Here are the general steps you would follow to create an ETL pipeline using Python, or any other appropriate programming language:

 

  1. Hire engineers – This may be a single engineer, or, depending on the scale of the data involved, a team of engineers.

  2. Procure the credentials – These are needed to access the data sources, and must be provided to your python environment.

  3. Write the extraction code – This is the code required to connect the various data sources.

  4. Produce the cleaning code – Write the code to clean and standardize the raw data.

  5. Write the transformation code – This applies the required transformations on the data.

  6. Create the loading code – Write the code to load data into the target destination.

  7. Perform ETL testing – Deploy the code securely and analyze the results.

  8. Set up monitoring and alerting – This is to manage your server’s resources and the scaling of the data.
     

 

Is ETL easy to learn?

 

A simple, one-off, ETL pipeline can be easy to learn and implement. However, the process quickly becomes complex as you add more pipelines and sub-workflows. Building one pipeline is easy, but building 50 is difficult, so ETL rapidly becomes harder as the scale of the data increases. Moreover, the need to execute multiple factors well, such as engineering, security, governance, monitoring, and maintenance, make a real-world ETL pipeline challenging to learn in practice.

 

Does ETL require coding?

 

Creating ETL processes from scratch requires a great deal of coding, as well as proficiency in multiple languages and technologies, such as Dev Ops, monitoring, data warehousing, vendor management, and credential management. As the number of pipelines – and the resulting complexity – increases, so too does the need for automation to control them. However, there are many ETL tools which make this process much easier. There are also ETL service providers, who take care of the whole process for you, so you never even need to touch a line of code.

 

Can ETL be automated?

 

As a reliable business process, ETL absolutely can and should be automated. Writing ETL operations code is complicated and error-prone, and this becomes difficult to troubleshoot in highly complex pipelines. An automated tool allows your engineering team to easily design, execute, monitor, and test an ETL pipeline, usually via a simple drag-and-drop GUI. Why write ETL code from scratch when it’s probably already been produced better elsewhere? The use of built-in templates and connectors minimizes the need for manual programming and oversight, making the ETL process much faster to create, and easier to maintain.

 

Why ETL is critical for your data strategy

 

As described in our guide above, ETL is a difficult process to implement correctly, and this makes it expensive and time-consuming to accomplish with an in-house team, especially as your datasets grow in size and number. If you want your ETL pipeline to be completed within budget and time constraints, it’s often best to invoke external resources and expertise. So next time you hear the words ‘it’s just ETL’ with your organization, don’t be afraid to push back. 


Switchboard is a ready-made data unification platform that enables enterprises to master vast datasets. Outsourcing your ETL needs may be the best solution for a complex workflow, so contact our team to discuss your needs today.