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We often hear data related buzzwords such as data science, machine learning, and artificial intelligence and assume that these things just work or happen around us because that’s just how technology is.
But there is actually quite a lot of work that must be done before any of those technologies can be leveraged.
In order to build intelligent data products, companies must engage in data literacy, collection, and infrastructure initiatives to collect, move/store and then explore and transform that data before it even gets aggregated for analytics purposes. Only then, companies can apply those hot buzzwords like AI and deep learning and make the data make sense.
We’re going to take a deeper look into what data engineering is, how it fits in the data hierarchy, how data warehouses work, and finally, explore the role of a data engineer.
What is Data Engineering?
Data engineering the process of creating interfaces that allow data to flow from various systems and be accessed by various members of a business organization.
Data engineers are responsible for creating these systems and are tasked with setting up and operating an organization’s data infrastructure.
Data Engineering and the Data Warehouse
Organizations often use different software products to manage various aspects of their businesses. This also means that there are many different databases with data scattered across the organization in various formats.
This can be seen as a type of inefficiency that can be solved by creating a unified storage system, where data is collected, reformatted, and ready for use. We call this a data warehouse.
So why would you want a data warehouse?
If your business relies on managing data in an intelligent way then you will want to see the big picture of your business, and if your product aims to help businesses see the big picture as well, then you will need a way for data to be stored in a uniform format.
With a data warehouse in place, data scientists, business intelligence engineers, and other employees can connect and access the data they need.
Data architects are responsible for building the initial data warehouse. This means deciding on its structure, data sources, and unified data format. Data engineers are responsible for moving data from one system to another, in what we call a data pipeline.
A data warehouse is a specialized central database that is optimized for querying large volumes of data. This makes reporting, analysis, decision making, and metric forecasting tasks much easier.
Data warehouses can be useful for a number of reasons.
- Data analytics is a CPU intensive task that can be risky when done on production systems. A data warehouse takes the potential load of data science tools in order to boost efficiency and to deliver insights faster.
- Data warehouses allow for tighter control of access to certain kinds of data and production systems
- Allows for cleaner production systems, while holding long-term data in the warehouse
Data Warehouse Structure
Databases come in three different flavors, on-prem, cloud or hybrid. There are trade-offs for each option and it will be up to the data architect to decide what is the best way to go depending on various factors within your organization.
Metadata serves to add context to data, allowing it to be easier understood and manipulated and contains historical data on its origin and previous transformations.
Access tools allow users to interact with the data warehouse. These tools can be specific to the type of user accessing the data and are able to limit the level of access a user has as well.
Data warehouse management tools can be seen as the wrapper that holds everything together as well as handling management and administrative functions. Data warehouse management tools exist to provide these capabilities to organizations.
What is the Data Pipeline?
While data warehouses are responsible for storing large amounts of data, data pipelines handle the flow and formatting of data. There are a number of different processes that data must go through before reaching the data warehouse. A data pipeline is the sum of those processes.
In large enterprises that deal with vast amounts of data, data pipelines are extremely beneficial.
For startups, implementing a data pipeline might be a bit extra unless you are dealing with technologies such as data science, ML and AI, otherwise, SQL can do the job just fine.
Data pipeline processes should be automated. Data engineers are responsible for maintaining systems, repairing failures, and updating those systems according to the needs of the organization.
Common use cases for data pipeline:
- moving data to the cloud or to a data warehouse
- wrangling the data into a single location for convenience in machine learning projects
- integrating data from various connected devices and systems in IoT
- copying databases into a cloud data warehouse
- bringing data to one place for business intelligence
Creating a data pipeline
A data pipeline is essentially setup steps or operations that data undergoes before reaching a data warehouse. Pipeline infrastructure can be broken down into the following set of ETL operations.
1. Extracting data from source databases
2. Transforming data into a unified format
3. Loading reformatted data to the data warehouse
Extracting raw data
Data engineers write specific jobs that take raw data from different database sources. These jobs operate on a set schedule and pull only a predetermined amount from a certain period in relation to the schedule.
Data coming from different sources have their own unique format which then must be transformed into a universal format that can be stored in the data warehouse. This helps to increase querying and analysis efficiency.
Once the data has been transformed and is now unified, it can then be saved into the system that serves as the single source of truth.
We’ve often talked about this single source being a data warehouse, but it can also be a relational database management system or even Hadoop, which is a framework that allows for distributed processing in order to solve problems using large amounts of data.
Maintaining the pipeline
Business needs are constantly changing, and so does the data required to achieve those needs. Data engineers must stay on top of the pipeline and add/delete fields to update the schema along with repairing failures and updating the pipeline itself.
What are Data Pipeline, Data Warehouse, Data Engineers?
Data engineers are responsible for building, maintaining, repairing and updating the data pipeline. They work together with data architects to ensure that data coming from various production systems in an organization are formatted correctly and stored in the data warehouse.
Data engineers are extremely bright and talented engineers that have advanced knowledge and programming skills to design systems for continuous and automated data exchange. They tend to be multi-disciplinary professionals that often work in teams with other data engineers, data scientists and BI engineers.
Working with data is challenging, as it can become corrupted and conflict with data from other sources.
Good data engineers will know how to carefully plan out and test systems that filter junk data, find and eliminate duplicates and incompatible data types, encrypt sensitive information while maintaining the clarity of important data.
When it comes to software engineering expertise, data engineers will most likely be very proficient in using Python and Scala/Java. Ideally, they should have a broad knowledge of the software development cycle including DevOps.
Knowledge of different types of databases (SQL and NoSQL) is a must, along with data platforms and concepts such as MapReduce. In addition, they will need to have some in-depth knowledge of various data storage technologies and frameworks in order to build pipelines.
Source: Ryan Swanstrom
Data engineers, or ETL engineers, understand very well how fragile data pipelines can be. After all, like real pipes, they can leak and even break causing problems downstream. And so there are a number of different factors that Data/ETL engineers are aware of.
Let’s look at a few examples.
Source data changes
Pipelines are initially built to work with certain data structures and schemas. Naturally, over time these criteria change as businesses evolve. Data engineers are mindful of this and take meticulous care in ensuring that these pipelines stay clean.
You can look at pipelines as a series of related processes where the output of one process becomes the input of the next. Knowing what data is being transformed as well as their outputs become extremely important in order to prevent unexpected outcomes.
Pipelines are changed and optimized from time to time. ETL engineers are aware that any changes to a pipeline can cause issues downstream. This is why testing is important to catch any harmful errors.
Corrupt data and network errors can break pipelines. Data engineers need to be able to have good reporting and tracking tools in order to locate the point of error and resolve it.
As data size grows, so does pipeline complexity. With more complexity comes slower processing speeds. Data engineers must have a process for measuring and optimizing performance when it’s appropriate.
Data engineers need to have processes to address scaling ETL processing. Dealing with input data size, processing steps, third party service invocations, and parallelized data loads are common when scaling a pipeline.
Hire a Data Engineer
Data engineers or Data pipeline engineers are extremely difficult to find. Many of the brightest talent in the United States already have been hired to work at companies like Google, IBM, Amazon, and Microsoft.
Tech giants have built relationships with academic institutions with strong data engineering programs to cherry-pick the best and brightest.
But that doesn’t mean that talent is unavailable, considering that there is the rest of the world to source from when looking for bright talent.
However, the challenge is finding the right data engineer that meets your requirements.
Why hire a Data Engineer
Trio data engineers are pre-vetted, interviewed and then trained further to become true software and data professionals, capable of adapting to situations that are both within and outside of the scope of their general expertise.
At Trio, we hold our engineers to a higher standard. Much like how elite special forces units recruit only the best from main branches of the military, we recruit engineers who either show amazing potential or demonstrate exceptional skill. We then take their talents and sharpen them even further.
Another benefit of hiring a Trio engineer is that you won’t incur the costs of hiring, which can add up to be around 30% of an engineer’s salary on average, as well as overhead costs associated with full-time employment.
By working with Trio, you can enjoy a highly experienced full-time engineer for a fraction of the cost, along with the added project management assistance.
To learn more, hit us up and tell us about your project so that we can get you started.
How to hire a Data Engineer
For those who wish to take the high road and hire data engineers on your own, we’re still here to help.
Hiring an engineer on your own is a very focused and hands-on process that requires considerable knowledge about data engineering in general.
The last thing you want to do is trust your hiring process to someone with no technical ability.
If you are a non-technical manager looking to learn a thing or two, we have a great resource here for you to learn more about the hiring process in detail.
Otherwise, we’d recommend you contact Trio for consulting and engineer allocation.
What to look for in a Data Engineer
At a high level, data engineers should be able to:
- Use Python, Java, Scala or Ruby to write processes
- Build pipelines that connect to data warehouses
- Use open-source or custom ETL tools
- Use ETL cloud services
- Work with AWS Data Pipelines
- Use tools such as Hadoop, Spark, Kafka, Hive, etc
How much do Data Engineers cost in the U.S?
The average salary for a Senior Data Engineer is $133,482 per year in the United States, according to Ziprecruiter.com data.
Here’s a chart that visualizes the salary ranges within the United States for a Senior Data Engineer.
How much do Data Engineers cost in South America?
Due to economic differences between the United States and South America as a whole, the cost of offshoring engineering is significantly lower than hiring full-time with U.S talent. For data engineers in South America, the average salary is currently around $100,000 whereas a mid-level engineer costs around $76,000.
How much do Data Engineers cost in Ukraine / Eastern Europe?
Eastern Europe shares very similar rates to South America, again due to the economic differences. When looking at salaries in Eastern Europe, data shows that a senior data engineer costs around $100,000 on average.
Hourly rates for Data Engineers
Another way to look at engineer costs is through hourly rates. While salaries are good to understand for hiring engineers for full-time and long-term, you might just need an engineer for a period of 3-6 months or 6-12 months. In these types of situations, it’s best to calculate your costs based on the hourly rates of an engineer.
Below is a table that lists the various hourly rates of engineers in different locations based on their job title.
ETL tools have been around for a long time and service companies of all shapes and sizes. The main principle to keep in mind when talking about ETL is latency. Two types of ETL are generally discussed within organizations: “Long Haul” and “Last Mile” ETL.
“Long Haul” ETL are long-term processes that can run for multiple hours. “Last Mile” is where latency really makes a difference, as it pertains to more short-term and lightweight processes.
ETL tools generally compete on speed and therefore invest their energy into building “Last Mile” ETL to increase the time to insight. In the end, that’s what all of this is really about.
Enterprise Software ETL
Informatica Power Center
Informatica offers an enterprise solution that is mature and well-regarded in the industry. They are also a 5 time Gartner Magic Quadrant leader.
IBM Infosphere DataStage
Infosphere DataStage is part of IBM’s Information Platforms Suite and InfoSphere. It’s also a very mature solution targeted towards the enterprise. It is built to work with multi-cloud and hybrid environments to support big data connectivity.
Oracle Data Integrator (ODI)
Oracle Data Integrator platform handles all data integration requirements: from high-volume, high-performance batch loads, to event-driven, trickle-feed integration processes, to SOA-enabled data services.
ODI also provides a declarative flow-based user interface for data integration and plugs in with other Oracle products.
Microsoft SQL Server Integration Services (SSIS)
SSIS is popular among SQL users and comes at a lower price point than some other enterprise solutions. SSIS features GUI and wizards for handling just about every ETL process there is.
Ab Initio offers a general-purpose data processing platform that checks all the boxes when it comes to efficiency, robustness, scalability, managing complexity, etc.
Operating under a single architecture, users don’t need to stitch together different technologies to get their pipelines up.
Ab Initio aims to provide an all-in-one solution to reduce complexity topping it off with a graphical approach to managing ETL.
SAP Data Services
Considered to be an internal tool in the SAP product ecosystem, Data Services is used mainly to pass data between SAP tools.
SAS Data Manager
SAS ETL product, Data Manager, provides a number of features beyond just ETL that allow organizations to improve, integrate and govern their data. It also has strong support for Hadoop, streaming data, and machine learning.
Open Source ETL
Talend Open Studio
Talend offers open source ETL and free data integration solutions for various points of the ETL process as well as other processes related to accessing and governing data. It’s by far the most popular open source product.
Pentaho Data Integration (PDI)
Now a part of Hitachi Vantara, Pentaho’s open source offers a no-code visual interface for taking diverse data and unifying it.PDI uses an ETL engine and generates XML files to represent pipelines.
Hadoop is a general-purpose distributed computing platform that houses a vast ecosystem of open source projects and technologies geared towards handling ETL tasks.
A Talend company, Stitch is another open source option that is cloud-first that provides simple and extensible ETL. It works with many different data sources and destinations and the platform itself offers strong extensibility, security, transformation, and performance capabilities.
SQL is a great option when your data source and destination are the same, and is capable of handling basic transformations. Since SQL is built into relational databases, you don’t have to worry about license fees and is widely understood throughout the developer community.
Java has extensive support for different data sources and data transformation. Being one of the most popular programming languages, it has a pretty active community, and you’ll find Java and Python being weighed against each other for various trade-offs.
Python is a popular option for performing ETL tasks and has a pretty strong community surrounding ETL engineering. Developers tend to go with Python over an ETL tool when building custom ETL due to its flexibility, ease of use, and available libraries for interacting with databases.
Spark & Hadoop
When dealing with large datasets, it’s possible to distribute the processing across a cluster of computers. Spark & Hadoop make this possible and though it is not as easy to work with, unlike Python, it can be very effective when you need to optimize latency on larger datasets.
ETL Cloud Services
Cloud services are an interesting point of discussion as companies like Amazon, Microsoft, and Google offer their own proprietary ETL services on top of their cloud platforms. There are obvious benefits with using cloud services, such as tighter integrations, and greater elasticity. You cannot mix and match cloud services with different cloud platforms.
Elastic MapReduce (EMR) is AWS’s distributed computing offering. It’s great for companies who like to run Hadoop on structured and unstructured data. EMR is also elastic, meaning you pay for what you use and is scalable, despite being difficult to use.
AWS Glue is a managed ETL option that is integrated into other AWS services (S3, RDS, Redshift). Glue features the ability to connect to on-prem and move data into the cloud.Developers can work on ETL pipelines based on Python and even run proprietary Glue-based pipelines as well.
AWS Data Pipeline
AWS Data Pipeline handles distributed data copy, SQL transforms, MapReduce applications, and custom scripts all in the cloud against multiple AWS services (S3, RDS, DynamoDB).
Azure Data Factory
Data Factory connects to both cloud and on-prem data sources and writes to Azure-data services. Hadoop and Spark are supported in Data Factory.
Google Cloud Dataflow
Dataflow is a managed service that allows developers to use Python and Java to write to Google Cloud destinations. It does not connect to any on-prem data sources.
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