How Astronomer Is Helping to Bridge the Big Data Gap
- by 7wData
No one is questioning the fact that data science is here in a big way, and it’s growing fast. Today, data science ninjas are perhaps themost sought after talent on the market. For those who aren’t clear on what exactly a data scientist does, data scientists use a combined set of skills from computer science, statistics, engineering, business insights, and strategy to mine enormous volumes of data and pull out key insights that have major impacts on businesses.
It’s easy to understand how companies have grown to understand the value of data science, as the volume of data generated continues to grow exponentially. According to IBM,over an estimated 2.5 quintillion bytes of data are created daily. What that means is that 90 percent of the data that exists today was created in the past two years.
Without data scientists, consumers wouldn’t have the personalized recommendations of Hulu or the shopping experience of Amazon. They wouldn’t be exposed to the highly targeted content and advertising on all their favorite social media platforms. For the businesses involved here though, these represent clear, compelling revenue opportunities that just don’t happen without strong strategy and execution around their data. The issue is that data scientists aren’t exactly a commodity.
According to aMcKinsey study, by next year the demand for data scientists in the U.S. alone will be half a million jobs. The problem is that there will be less than a quarter of a million data scientists available, globally. To make the problem even worse, the majority of most data scientists’ time today is spent doing data engineering, which simply sets the table for them to start doing real data science.
Maxime Beauchemin, a data engineer at Airbnb and architect supreme behind Airflow, defined data engineering as:
Data engineering exists because companies now have massive amounts of highly valuable data, but to gain valuable insight from big data, it needs to be extracted and made sense of quickly and at scale. As far as specific needs or functions within data engineering are concerned, many can be categorized within data integration and services.
With SaaS becoming the new standard for company operations, data integration has become as important and as challenging as it’s ever been. There is a critical need to synchronize referential data across systems, and the need to have up-to-date data to function properly is a much bigger one than within SaaS.
The fact of the matter is that often times today’s executives are still signing deals without really thinking through or understanding the data integration challenges. Now, some SaaS providers will offer their own analytics offering, but they’re inherently lacking in understanding and perspective of the rest of a customer’s data infrastructure. It doesn’t make it any easier on executives when the amount work that it will actually take for proper integration is typically downplayed by vendors to help them close more deals, faster.
A step further, data engineering can require developing services and tooling to automate work that data scientists may have previously done more manually. Services and tooling for things like data ingestion, metric computation, anomaly detection, metadata management, experimentation and instrumentation are common examples of this. There needs to be a constant priority to automate workloads and build abstraction that allows data scientists to climb the ladder of complexity.
Sounds like a lot of work, right? Well, we happen to know some folks that, rather than letting a shortage of talent be a crux to improvements in this space, have built their own software to eat the world with technology to solve this problem.
Astronomer powers data intelligence by unifying data to reveal actionable insights. Their platform connects and centralizes data, making it remarkably simple for anyone from business users to data scientists to quickly create and monitor data pipelines.
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