Data Lake to Pipeline Data Storage Deficiency and IoT and AI Platforms

If you are a data scientist or even if your field is remotely related to data analysis, you must be aware of the data expansion that occurs at every minute. Previously this bloom doubled every five years whereas now it is every year. It is expected to reach 44 trillion gigabytes by 2020. And while automation, increased adoption and the integration of AI and IoT ease several industrial processes, they also generate big data and large data hubs. Hence data is a new asset like coal and gasoline.

AI relies on data processing, analysis, and application-based usage. On the other hand, IoT generates large volumes of data from several devices it is connected to and the complexity of tasks being assigned. Both technologies thrive on data. These chunks of data merge into gigantic data pools that are not easy to manage nor control. Thus making it difficult for organizations to exploit it at its fullest potential. Regardless of the application, the more complex is the task, the more data it needs to store and process and bigger data pool. This opportunity to capitalize on the requisite to store valuable data led to the birth of data lakes.

The term “data lake” is coined by James Dixon, the CTO of Pentaho. He used the following analogy to explain the concept. “If you think of a datamart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake and various users of the lake can come to examine, dive in, or take samples.”

A data lake is a vast pool of raw, granular data, the purpose for which is not yet defined. This is generally unstructured data. Hence we need specialized tools and data scientists to understand and translate it for any specific business. The main benefit of data-lake is that it is easily available and can be quickly updated. While storing data, it associates itself with identifiers and metadata tags for faster retrieval.

The dataset collected by IoT from device sensors must be continuous to facilitate real-time perspicacity. Since data lakes are a good choice for people who wish to accumulate raw data from a variety of sources, without defining which data they’d prefer ingested in sequence, It a useful storage solution for these models, as patterns can be triggered as new models are injected. As it works on the principle of schema-on-read it can store data in its native format with no fixed limits on account size or file. Thus saving massive amounts of time and hassle. Bonus, when well maintained, it minimizes the in-house expertise and resources needed for smooth functioning. This frees up IT teams to focus on more pressing projects, which saves organization costs in the long run. Though building a full-fledged data lake can consume an enormous amount of time, one can opt for already available online storage facilities like AWS Lake Formation by amazon, Azure Data Lake Store by Microsoft, etc.

A data lake, augments analytic performance, product ionizing, business agility and native integration. When assimilated in machine learning, it helps to make better, profitable predictions in transportation, flexible solution in education, deliver real-time insights in healthcare. In short, the ability to support a large range of use cases. Because of its automated storage tier, data can be moved between storage tiers. This further optimizes data storage while reducing the cost implications. Due to a lack of silo structure, it provides a more robust analysis. It can also use open-source tools like Hadoop or Map Reduce. It reduces the long-term cost of ownership while allowing centralization (with shared copyrights). This can help different companies, a platform to share and retrieve data across organizational boundaries. However, there is a risk of security and access control. Sometimes data can be placed into a lake without any oversight, as some of the data may have privacy and regulatory need. While on-premises data lake requires frequent software upgrades and attention to physical hardware it will be sensible to choose a server-less one. The later allows IT teams to scale effectively.

As the concept of data lakes gains momentum, its design should be driven by what is available instead of what is required. We need to find out which solution suits best and employ it . Soon data lake will be a significant joint in data analytics, Machine learning, IoT and AI. Hence it is imperative to have a scalable, robust platform that saves time and costs while steering towards future possibilities.