Data is everywhere, and it is one of the most potent aspects of a business today.
Data-driven decisions are influencing the way we work and live. From machine learning, data science, and advanced analytics to real-time dashboards, all decision-makers have been demanding data for making the right decisions. Companies like Amazon and Google – the juggernauts of data – are taking over traditional businesses by leveraging data.
Due to its immense volume, variety, and velocity, the old processes and systems cannot support the dynamic data needs of businesses in the modern world. For advanced analytics and AI, veracity is a huge problem where the GIGO (garbage in = garbage out) principle is essential. This is because it is virtually difficult to analyze whether the data was the problem that caused wrong decisions, or the model was at fault.
To support these challenges, a revolution is happening in the data management space around how the data is processed, stored, and managed to enable decision-makers. Big data offers cost efficiency and scalability of greater magnitude than previously possible.
The data lake is a daring new approach that harnesses the power of big data technology and marries it with the agility of self-service. Most large enterprises today have either deployed a data lake or are in the process of deploying one.
Enterprise data lake is no longer just an option but an essential need for businesses as it helps them eliminate multiple data management headaches.
For managing burgeoning volumes of data seamlessly, enterprises need a solid plan. Read on to find out how to have one.
What is a Data Lake?
As the name suggests, a data lake consists of a large data repository stored in its raw and natural form. All the unstructured, semi-structured, structured, and binary data is stored with the primary aim of core analytics.
Reasons To Implement An Enterprise Data Lake
Here are some of the reasons which state why it is essential to implement a data lake and how it helps in eliminating data management hurdles.
1. Data Lake Seamlessly Adapts to Needs
The biggest perk of a data lake is its very flexible architecture and variety. This means enterprises can store every type of data, such as log data, XML, data from sensors or IoT, data from social streams, etc. Moreover, data lakes are considered schema-free – an important factor for data to get analyzed in its most unsorted or raw form. This flexibility helps in data aggregation across multiple business domains and processes, opening new possibilities for analysis.
2. Helps Breaking the Silo
98% of businesses claim that data silos are the most pressing issue that impacts the business in real time. Data lake becomes a one-stop repository for the organization to break data silos. It also enables the organization to cross-analyze the data from multiple sources to give a contextual view of any situation. This eventually helps in making the information easily accessible to the masses.
3. Data Lakes Can Be Scaled
The common notion in the industry is that – scaling of data involves higher costs. But data lake is an over-the-counter and inexpensive solution that makes data scaling super easy compared to the traditional data warehouse.
4. Better Analytics With Data Lakes
The feature of just-in-time schemas help proficiently in running the analytics fast on data lakes. Data lakes enable real-time data analytics, which is considered very difficult in the case of conventional data storage solutions. Quicker ingestion of the raw data sources helps the business analyze them on demand.
5. Comprehensive Dashboards
Data lakes have tools to plug-and-play available for businesses to build dashboards. Without relying on the development team, analysis and reporting is significantly quickened.
Technical Enterprise Data Lake Advantages
1. Storage of Data in Native Format
A data lake can eliminate the need for any data modeling during ingestion. It also provides utmost flexibility to give crucial insights into the business.
2. Versatility & Scalability
The one significant advantage of the data lake is its immense scalability. Data Lake can easily store data from diverse sources. Moreover, it is far lesser expensive compared to traditional data warehouses.
3. Schema Flexibility
The traditional schema required the data to be in a prescribed format. For analytics, this is a huge drawback and an obstruction, as the users need to analyze data in its original form. All the products of traditional data warehouses were schema-based, but the enterprise data lake is schema-free. Users can easily define the schemas (multiple) for the same data. In more straightforward terms, data lakes enable the users to decouple the schema from the data, which is a boon for analytics.
4. Supports Multiple Languages
One of the major headaches of businesses has been that traditional data warehouse technology always supported only SQL and ignored other languages. Although SQL is a great language, it is primarily suitable for basic analytics. But in today’s time, basic analytics does not suffice. The most prominent feature of data lake is that it provides multiple languages and options support for analysis. For example, it has Hawq, Impala, and Hive, which support SQL and have the relevant features for the business’s advanced needs. For instance, users can use PIG for analyzing the data in a proper flow. Similarly, to carry out machine learning, users can use Spark MLlib.
Adopting an enterprise data lake requires full-fledged support from the business. Businesses must set the correct expectations, have a robust implementation plan, and communicate clearly with their stakeholders. Companies will require much more than mere technical expertise to achieve this.
