Photo by Nathan John on Unsplash Managing big data no longer means just buying bigger and faster servers It now also means needing to understand the concept of parallel computing. The list of tools and data systems that are helping manage this specific concept continues to grow on a yearly basis. Whether it be using AWS and querying…
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Photo by Sean Pollock on Unsplash Banks from Seattle to New York trust us with their data science solutions. Why’s that? The short answer: while the finance industry has been leveraging data science and statistics for decades, times are changing. Data science and financial strategy are evolving. This means teams need to stay current in order to maximize…
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Photo by Sean Pollock on Unsplash You may think of ATM workings as a revolutionary experience but since the advent of Fintech, the entire financial services domain has entered a new era. Whether you purchase a cup of coffee or manage your finances, fintech is everywhere. From payments via apps such as Payoneer or Paypal to getting reports,…
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Python really is everywhere at this point. Although many gatekeepers argue whether a person is really a software developer if they don’t code in a language more difficult than Python, it still is everywhere. It’s used to automate, manage websites, analyze data, and wrangle big data. As data grows, the way we manage it becomes more and…
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Back in my day, databases and applications used to only sync late at night while everyone was asleep. Now in the modern era, everyone expects their data the second it’s updated (if not somehow magically before the data occurs). Large corporations and Fortune 500 companies depend on this data to be able to predict consumer…
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Photo by ZACHARY STAINES on Unsplash Performing ad hoc analysis is a daily part of life for most data scientists and analysts on operations teams. They are often held back by not having direct and immediate access to their data because the data might not be in a data warehouse or it might be stored across…
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Photo by Taylor Vick on Unsplash How companies manage their data is no longer limited to traditional relational databases. Amazon Web Services (AWS) for example offers a diverse collection of options when it comes to storing data. We recently wrote a piece solely focused on Redshift, but we wanted to introduce a few more options.…
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Photo by Jezael Melgoza on Unsplash As an organization grows, its data storage, monitoring and analysis requirements also exponentially increase. Traditional data warehouse don’t always easily handle massive amounts of growth. This caused a need for alternative solutions, starting from the mid 2000s. One such solution is Amazon Redshift from Amazon Web Services. What is Amazon…
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Amazon Web Services (AWS) has simplified much of developers’ workflows and development over the past decade. AWS allows engineers to command and control cloud-based infrastructure, data, and other technical pieces of infrastructure without the hassle of developing entire frameworks from scratch. Initially, AWS was launched to take care of online retail operations for Amazon, but…
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Photo by Marcin Jozwiak on Unsplash We recently wrote about ETLs and why they’re important. We wanted to provide an outline for what ETL tools are. You could refer to these ETL tools as workflow tools that help manage moving data from point A to point B. Two of these popular workflow tools are Luigi by Spotify and…
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