Data Lakes for Big Data ⛵️
Volumes of crude data are available at our fingertips today, and the latest concept of a #DataLake helps store any type or volume of data as-is, process it in real-time or batch mode, and analyze it at scale 🤽🧵👇
Volumes of crude data are available at our fingertips today, and the latest concept of a #DataLake helps store any type or volume of data as-is, process it in real-time or batch mode, and analyze it at scale 🤽🧵👇
— Sculpting Data: First Act of Machine Learning 📖 (@DataForML) January 26, 2022
DATA VARIETY 🍏🍌🍇 There is no schema definition or a structure in a #DataLake during the capturing stage and therefore raw data from multiple sources can be streamed in real-time into this centralized repository without a need for any #DataTransformation.
IMPLEMENTATION ☁️💾🔢 Since a #DataLake can hold 100 terabytes or even petabytes of majorly enterprise data, they are often implemented in cloud-based, distributed storage systems.
VALUE ADDITION ⏳⚱️🤓 Data is the food for learning algorithms, and a #DataLake provides the perfect solution to harness the knowledge from data across multiple resources, in a lesser time frame leading to in-depth analysis, involved collaboration, and rapid decision making.
ALL IN ONE 🤖🥇👀 Especially with #MachineLearning, a #DataLake provides a one-stop solution for enterprises to use historical data, hypothesize, generate insights, refine assumptions, and assess results without needing to go outside the system.
BENEFITS 🎟🎞📝 Besides simplifying #DataManagement across structured, semi-structured, and unstructured formats, like texts, images, SQL tables, etc. a #DataLake helps lower the cost of ownership, speeds up #DataAnalytics, & provides a solid foundation for #BusinessIntelligence.
CHALLENGES 🧩🤸🏻♀️🔐 Because of the big data variety in #DataLake, it is important to develop mechanisms to catalog/tag data to make it easily discoverable. Additionally, for security/privacy & #DataGovernance access controls, network security, and encryptions should be invested in.
EXAMPLES 🐠🎯🔢 Microsoft @Azure, Amazon @awscloud, @qubole, @Informatica, etc. provide some of the popular #DataLake solutions, each with its own set of prominent features, and one can choose based on their specific use case.
On that note, if you are someone who is still getting started on #DataForML and need a boost to your #MachineLearning dataset building skills using #Python and all things #OpenSource! Check out @DataForML on @amazon 🛒