How New Data Storage Technologies Simplify Big Data
Big data is the mother of all buzzwords in the corporate world and when you talk about Big Data its foundation – data lakes – always work their way into the conversation. Unlike the short shelf life of buzzwords, data lakes aren’t going anywhere. New data lake storage technologies are constantly evolving to keep up with AI and machine learning, the drivers of big data. The ‘why do I need a data lake’ answer has more to do with a power-packed solution to advanced analytics than just the IT arm of saving money through economical data storage.
Without a data lake could big data analytics be possible?
Refusing to address a company’s need for a data lake is ignoring the evolving business landscape, putting the business in danger of being left behind. You don’t need a data lake to store payroll data but it is a necessity for event-based streaming data which is now very commonplace. Semi-structured data generated by sensors in IoT devices or unstructured data from social data logs, emails and call logs constitute over 80% of business data. Ignoring this large chunk of information can be disastrous. These types of data are created in small bursts and at high velocity. Traditional schema-on-write data warehouses are ill-suited to cope with such new, quick-moving data trends. You need a data lake that allows all data to be stored in its original form and called upon as needed.
For example, if you need to know the total revenue for the last 6 months from your south-west region, your existing databases will be able to give you the answer in quickly. So why bother with big data and a data lake? If you wanted to know the detailed customer journey of your highest-value customers from the same region, that would be a different kettle of fish. For this, your business would need to pull data that is not uniform or even well-defined. You would need customer calls records and email messages going back as far as possible. Only data lakes can store such information. A data warehouse can give you numbers but not the information behind the numbers such as why customers have left or become even more profitable.
A data lake can break down data silos, allow for smooth access to numerous different data sets, enable data exploration through analytical sandboxes, and fuel Predictive Analytics. Data lakes have also made it possible to dig up insights from unstructured data, making them an essential component of any business-intelligence program.
Evolving data lake storage technologies
So how are data lakes able to store such vast amounts of data? The underlying architecture of data lakes is what makes such a massive data storage repository possible. Find out more about how data lakes work here. To cut a long story short, data lakes are made of two main components, storage and computing. The initial raw data ingestion layer and subsequent storage are just the beginning. Computing tiers can be added on at any time. The beauty of data lakes is that business can begin a proactive, future-focused strategy today without worrying about how to use all of the data right away. The bitter truth is that if you don’t save your data, it won’t be there when you’re ready to benefit from it. Future-proofing your business is practical particularly because a data lake, unlike a data warehouse, does not require any complex software or additional employees.
Data lake storage technology, just like data warehouses, were originally on-premise with dedicated hardware. Since then ‘data as a service’ has seen data lakes move to the cloud. This effectively means that businesses no longer owns or maintains the servers, but can use as much data storage space as they need and pay as they go. This makes scaling up operations easier and separates storage needs from business computing, which results in better security and lower risk. Sertics leverage Amazon AWS to help you combine your storage with data lake tools for harvesting powerful business insights.
Beware your data lake doesn’t become a swamp
Data lakes might be the answer to all Big Data needs but they can easily turn into a data swamp if they’re not organized. Find out more about the dangers of a data swamp in our blog, Data Lake Management: How to Prevent a Data Swamp. Massive data sets cannot be organized effectively by hand, but machine learning makes this task simple. Machine learning, through iterative steps based on known analyzed data sets, will train specific models for future categorization of unknown data that will flow in. Each data element in the data lake will be assigned a unique identifier and an extended set of tags. When a user queries the data lake with specific metadata, all data is analyzed and the right data is pulled forth to answer the inquiry. Machine learning is the superman of data lakes. Not only does it makes tagging of data easier but it also fuels Predictive Analytics to identify patterns in enormous data sets.
Sertics is a software as a service provider for data lake creation, data storage, data visualization, data lake management, and predictive analytics. Sertics also utilizes other emerging technologies like machine learning and artificial intelligence. Learn more about Sertics by contacting our team and scheduling a product demo today.