The Emergence of Big Data

Since the advent of personal computing, computer users have been contributing, voluntarily or involuntarily, to an ever-growing database that records everything from website analytics to sales transactions. More recently, modern innovations enable us to track complex activities such as population movement trends and aviation traffic. As we proceed through this Information Age, technology is obtaining many more angles from which to record data. As a result, organizations are finding more uses for the information gained from analyzing this data in order to further their interests. In today’s competitive markets, the utilization of data is a key factor in determining who wins and who loses, but as the power of the internet grows exponentially, so does the amount and practicality of data. This sets the stage for what many call “Big Data”, a new concept that is already changing the status quo in analytics.

A New Paradigm of Data

Big Data encompasses technologies and processes involving data that is too complex, large, or frequent for conventional databases to easily process and interpret. Similarly, analyst Doug Laney famously differentiates Big Data from normal data in three aspects: volume, velocity, and variety (or the “Three Vs of Big Data”, as many call it).

  • Volume: A little over a decade ago, we thought 100GBs of data as being plenty, but due to the adoption and evolution of computer technology in many industries, exabytes of data are now produced daily. In the year 2012 alone, about 2.5 exabytes of data was produced everyday (that’s 2.5 billion gigabytes)! Some companies alone produce hundreds of terabytes daily.
  • Velocity: Big Data doesn’t come in a stream — it comes in torrents! When managing city infrastructure, surges of data are recorded on a per-second basis. The Large Hadron Collider produces about a petabyte (1000 terabytes) per day. Gaming companies produce hundreds of thousands of user logs per second!
  • Variety: Big Data encompasses both structured and unstructured data. Before the world went digital, data was neatly organized and structured, and was thus easily recorded, processed, and analyzed. Today’s internet is more complex and produces tons of videos, images, and instrument data, a few of the many types of objects that can’t be as easily organized or searched.

Utilization of Big Data

This isn’t just an idea — 84% of those surveyed in IBM’s and Accenture’s Industrial Internet Insights Report for 2015 believe that the use of Big Data analytics will be a major game changer in their respective industries and in the upper-echelons of many organizations, analytics are a top priority. Far into the future, Big Data will continue to be a key differentiator in many markets and to gain a competitive edge, companies are already attempting to tap into this trove of information for discovery, insight, and value. From retail to healthcare and even enterprises, the benefits of utilizing Big Data are already being realized.

  • Websites: Webmasters can track user behavior on their websites and marketers can use this information for narrower targeting.
  • Healthcare: For healthcare providers, information for just a single patient can come from different sources such as physician offices, hospitals, laboratories, and insurance companies. Managing millions of records of patient data is troublesome but by utilizing Big Data technologies, providers can build better healthcare systems, improve outcomes, and increase access.
  • Companies: Big and small companies alike can benefit from Big Data analytics. Logistics and business operations can be further optimized by collecting massive amounts of user data, such as geographic and social networking data to track trends as well as user behavior data to identify consumer demands and stock inventory accordingly.
  • Mobile Applications: Smartphones and many mobile applications are created to serve millions of users and the data they create can be used to further improve user experience and performance.

As great as it sounds, there are growing pains when it comes to the evolution of data and it’s becoming increasingly difficult for organizations and businesses to store and analyze “Big Data” without paying much money. After all, hosting tremendous amounts of data requires space, time, and money to build and maintain data warehouses. Even if a business decides to scale their own server, capacity will always be finite and continuously adding capacity ultimately exhausts resources.

The Shift Towards Big Data Analytics

For data-driven companies like Facebook, Twitter, and Yahoo, Big Data collection and analysis are a top priority. Both Twitter and Yahoo use Apache’s open-source “Storm” and “Hadoop” projects for processing large streams of data and Facebook, whose data warehouse is one of the largest in the world, utilizes its own “Presto” open-source system for processing hundreds of petabytes of data. Currently, Presto is successfully used by over a thousand employees, who run tens of thousands of queries a day (processing about 1000 terabytes daily!). By harnessing the convenience of cloud and open-source technologies, Big Data systems from all over are frequently scaled, optimized, and upgraded with ease and in accordance to the scale of data and needs of the company.

With large and renowned companies leading the way, the use of Big Data and cloud computing is becoming widespread, but at the same time, the costs and knowledge required in order to manage it is making it difficult for many companies —especially smaller companies with less capital and labor— to take advantage of Big Data. Fortunately, services such as Amazon’s “S3” and “Redshift” as well as Apache’s “Hadoop” framework are shouldering the burdens of Big Data by handling data warehousing and maintenance, offsetting costs and making it easier for companies to handle large amounts of data and run queries against it. Because these services are utilized “over the cloud”, users don’t need to worry about the limitations and labor that come with scaling up servers. Many data warehousing services also backup your data in multiple regions, so your data is safe during disasters or blackouts.

Moving Your Data, On The Fly!

With S3 and Redshift, storing Big Data is as convenient as ever, but in the long run, properly getting your data onto the cloud takes much work in terms of the extraction, transformation, and loading of data. This process is still costly and time-consuming for your team. Querying data takes much time and will take longer as the amount of data increases —many companies run queries over the night and the weekend! Each part of the process, or the “ETL” process, has its own constraint on the flow of data towards your data warehouse:

  • Extraction: Data accumulates on your servers. Over time, this will become overwhelmingly time-consuming to extract and the information that you infer from your queries will be inaccurate and outdated.
  • Transformation: To properly transform data into a format that the Redshift systems can store, scripts must be created, maintained, and adjusted frequently. Again, this takes time, money, and resources to achieve.
  • Loading: Before finally loading your data onto your data warehouse, there is still the possibility of encountering upload errors and ultimately losing your data.

A common form of data stored in Redshift is server logs. If you collect Big Data in a MySQL database, FlyData Sync can replicate your data onto your Redshift cluster by using the MySQL binlog. Once Sync is configured with your Redshift cluster, any change that is made in the binlog related to the data or schema of the tables that you replicated is also made on your cluster. With Sync, you can also automate the creation of reports and charts, as well as automated email alerts based on changes to your data, using the tools provided by Redshift.

With FlyData’s services, you can save valuable time and resources while increasing the value of your data. With faster and easier access to your Big Data, you can analyze real-time data with ease, create valuable insight, and improve your organization. To learn more about how having FlyData integrate your data helps your business, learn more about our customers or contact us.

About FlyData Team:
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