Over the last few decades, Big data analytics has become an essential element for businesses from a decision making perspective. Nowadays, the number of Big data analytics projects are increasingly high. However, it solely depends on how well the analytics has been applied. Furthermore, as per the market research, it is predicted that half of them have a high chance of failure to deliver the result as per expectations. In this context, Data Lake has received immense popularity in recent years. It addresses some of the challenge areas of Big data and data governance-related issues.
There are two main reasons behind it as per Gartner. One is the lack of proper direction in Big data projects. For example, landed on Big data projects without deciding on adequate use cases, the relevance of data for the particular business can completely ruin the analytics result. Besides, it must be observed that how well the data is being refined. Proper data preparation is another key aspect of any Big data project, which is also related to data governance.
Secondly, the issues related to Big data governance and management. Though data warehousing is a key player here, it has some drawbacks. It supports batch workloads where thousands of concurrent users can perform advanced analytics and essential reporting operation with a pre-defined data model. However, it needs a lot of cleaning and some more additional work before data modeling.
Related post – What is a Data Lake – A brief Overview
Problems associated with Big data
Before we introspect to the remedy, we need to know what the issues are with Big data. So, let’s start with what are the challenges in Big data? Well, from loading to transformation and generating the end data, Big data faces several challenges.
Issues:
- As technology advances, the variety of data also expands. For example, IoT is one of the prime sources of Big data today. However, such data can be of any format—for example, sensor data, smart data, weblogs, etc. Transformation of the source to target data for mapping is a challenge here.
- Sometimes, existing ETL processes do not work as a viable solution.
- For the unknown format of a new set of data, it becomes a question of the defined data set.
- In the transformation journey, many times, the exact meaning of the source data gets lost due to translation.
- Maintenance and analysis cost of data is high.
- Due to data silos, it creates inconsistency and sync issues in data.
- Since businesses are changing rapidly nowadays, maintaining data organization and optimized analytics become a challenge.
Benefits of Data Lake architecture
The architecture of Data Lake is like “Hub and Spoke." Thus Data Lake enables analytics as a service through this “Hub and Spoke” architecture. The following benefits of Data Lake enable the data science team to exploit analytics. It helps to develop the prescriptive and predictive analytics necessary to optimize key business processes. Also, it supports the data science team to explore, discover, learn, and refine the customer engagement model.
Benefits:
- Data Lake is a centralized schema-less data storage. So it can store raw (as-is) data. As a result, we don’t need to worry about the format of the data.
- Due to schema-less architecture, rapid data ingestion with appropriate latency is possible in Data Lake.
- A Data Lake not only provides faster ingestion but also executes batch processing at scale.
- Data Lake supports most of the analytics sandboxes like Hadoop, Spark, Hive, HBase, HDFS, etc.
- It supports most of the Analytical tools (SAS, R, H2O, Mahout, MADlib)
- It supports most of the Visualization tools (Tableau, ggplot2, DataRPM)
- Data Lake supports most of the Big Data processing tools (MapReduce, YARN, Elastic Search, HAWQ, SQL)
- It has the ability to map data across sources and also provides visibility and security to users
It provides the ability to manage data security, permissions, and masking.
- Supports self-provisioning of compute nodes, data, and analytic tools without IT intervention
- Data Lake encourages a common data model. Here, the business meaning of data is important than its physical structure and underlying technologies.
- As data is stored in its ‘as-is' format, so there is no need to revise the existing data model or to redesign the database.
- We can source and connect external data in a real-time manner.
Data Governance in Data Lake
Many Big data projects fail due to improper data governance. However, Data Lake answers that with a logical and physical separation of data, which is usually conceptualized as zones of Data Lake. This also helps to maintain the agility, flexibility, security, and organization of data.
A Data Lake handles all kinds of data –
- Structured
- Unstructured
- Historical
- Governed
- Ungoverned
- Lightly governed
Besides, Data Lake successfully addresses the 6V’s of Big data –
- Volume: terabytes, exabytes, petabytes, and zettabytes of data
- Velocity: able to capture and process streaming data in seconds or fraction of seconds.
- Variety: structured, unstructured, sensor data, multimedia data, text, audio, video, meter data, HTML, weblogs, emails, etc.
- Veracity: Ungoverned data which has ambiguities.
- Variability (data in change): Data in change that flows in a highly inconsistent manner.
- Value: The relative importance of data to the decision-making process.
Data Lake overcomes the drawbacks of a data warehouse
This is a workable solution for Big data storage. As mentioned earlier, Data Lake is capable of storing data in its raw format. It offloads traditional data warehouse tools by becoming a natural ETL choice (extraction, transformation, and load). Data Lake is suitable for rapid ingestion, federation, batch processing, transformation, and enterprise big data loading.
Conclusion:
Since big data volume and usage keep growing with big data initiatives, Data Lake sizes keep growing. However, organizations need to invest in Data Lake architectures and implementations strategically. Data Lake can leverage the power of six Vs. With its rapid ingestion of raw data. Additionally, it can help with data discovery and batch analytics. This can promise the benefits of the big data ecosystem. No doubt, the Data Lake is one of the key solutions for the Big data place's overall data strategy.