Nowadays, most of the organizations are under great pressure to capture, manage, and control both new big data and huge volumes of old enterprise data. At the same time, most analytic apps demand that old and new data should be combined at scale to allow extensive data exploration and analytic correlations. The ‘data lake’ has come forward as a new data-driven design pattern for persevering huge data volumes characterized by diverse types, sources, structures, containers and frequencies of generation. When designed well, a data lake an effective pattern for capturing data types, both old and new, at a large scale. A data lake is often optimized for the rapid ingestion of raw, detailed source data for exploration, analytics, and operations.

Data Lake Eco-System

During recent years, Data Lake has been recognized as a modern design pattern which fits today’s data requirements and the way it can be used. For example, most of the users want to ingest data into the lake rapidly so it is immediately available for analytics. They want to gather and store all the data in its original raw form so they can process it in different ways as per their requirements for analytics and operations. The need to capture big data, unstructured data, and data from new sources in a single pool such as IoT, customer channels, social media and external sources. As data lake when deployed correctly, can help with all these trends and requirements, if users can get past the challenges involved in it. Data Lake is still very new, so its best practices and design patterns are now emerging.
One of the hottest areas in data modernization is adding Data Lake to greenfield and pre-existing data ecosystems. A designed pattern is a repeatable and generalized approach to the commonly arising situation in IT solutions. The design arrays in data-driven lakes serves as confined data constructs such as tables, records and models. They can also serve as data planning which is the mixture of numerous design patterns and components. There is a common myth that data lakes need open-source Apache Hadoop. Well, most of the data lakes deployed today are on Hadoop, but it is not the only platform available for Data Lakes. Due to the huge size and variation of data in a lake, and the methods in which data must be processed and remodel, Hadoop has become a vital qualifying platform for data lakes.

Data Lake and Data Ingestion

Data lake ingest data very quickly. It ingests data in its original, raw form, straight from the sources with no cleansing, remodeling, transformation, and standardization. These data lake practices and other data managing practices could be then applied flexibly to the raw data. Users nowadays want to conduct analytics with relatively fresh data, so it’s vital to have data-driven design patters optimized for continuous, quick and automatic data ingestion. This is the reason why companies are turning to data lakes. The early data ingestion means that the operational data is accessible for exploring, discovery and reporting. A solo data lake can perform so many functions such the trend in the data warehouse is to use Data Lake as an improved zone for data staging and landing. Data lake’s early ingestion and late processing are similar to ELT (Extract, Transform, and Load). Adopting early ingestion and late processing allow the combined data to be available for operations, reporting, and analytics.

Data Ingestion Challenges

One of the main aspects of delivering value through Data Lake is to ensure that it is ingested efficiently. With the adoption of Data Lake, organizations are facing challenges – fast ingestion of different data types and metadata management for big data. Faulty ingestion pipe can emit incomplete and inaccurate data for the data lake. There are two reasons for ingestions which is often overlooked. First, it’s not as attractive as data analytics. Second, data injection is often perceived as easier to manage. Data ingestion is one of the major challenges faced by companies as they struggle to build better analytics.  As Data Lake is fed upon different tributaries of batch and streamed data, the value of analytics should be accurate, complete and consistent. Big data violates the assumptions which work for the traditional transaction data. You cannot expect the data characteristics for big data to be stable. The truth is that a major aspect of big data is data drift – unpredictable, unannounced and unending data mutation. Furthermore, the tools for big data ingestion are also immature as compared to the traditional data.  The most prominent tools are Apache projects which took inspiration from Facebook, Google, and LinkedIn.

Benefits of Data Lake

Data lakes offer numerous potential benefits. For instance, the capability to store the amount of data in the lake is significantly less than a managed warehouse is one of the major benefits. While looking at the solutions, the costs of storage are the major concerns which need to be reflected upon and given the relatively unstructured nature and form of data which can be kept without prior processing, it means that Data Lake offers significant financial value. Based on the fewer storage costs and massive variation in data types, it offers companies a chance to hoard their data. This means that in case a specific data set has no actual impact, it will have a substantial impact in the near future. With the given pace of change in technology, the capability to collect information in its native format before being introduced to a more structured and controlled database, it will be easier to use in the future system.  Because of the accessibility and the amount of data stored in data lakes, it offer clear benefits for sharing the data across the organization

The Bottom Line

It is significant to implements a proper ingestion infrastructure which allows data lake a complete, well-timed and consumption-ready data. Unlike a data warehouse, data lakes excel at utilizing the availability of huge amounts of coherent data to improve real-time decision analytics. It is not only useful in advanced predictive analytical applications, but can also be productive in reliable organizational reporting, particularly when it contains different data designs.