Data structure acts as storage by storing a large amount of structured and unstructured data in its native format without any fixed limits. It is a cost-effective method for the organization to save their high quantity data and increase the analytic performance. It can be used for later processing and finding meaning patterns by a research analyst. The flat architecture of Data Lake gives a unique identity and tag with metadata information
Why Data Lake?
A data lake isn’t a hierarchical data warehouse where the data are stored in files and folder; instead, it offers an unrefined view of data. The storage engines make it easier to enterprise-wide scheme data without the need for model data. The quality of analyses keeps increasing with the increase of data volume and metadata. Data lake also offers business agility by providing profitable predictions with the help of Machine learning and artificial intelligence. Data Lake makes the analysis more robust by providing 360 degrees view to the customers while offering a competitive advantage to the organization.
Data Lake Architecture
Now let us understand about figuring the data lake architecture.
The lower level representing the data generally remains at the rest while the upper levels show the real-time transactional data. These are the following tiers in data lake architectures-
Ingestion Tier: It remains on the left side to depict the data sources which could be loaded into batches. It gets the data from different sources with the help of connectors and loads it into Data Lake.
Insights Tier: These remain on the right side of the tier to represent the research side of the insight used by the system. HDFS can be used for the structured and unstructured data that is in rest. Next, the distillation tier converts into structured data from the storage.
The processing tier generates structured data with the help of analytical algorithms, and user queries in real-time for easier analysis.?
The unified operations govern management and monitoring, which includes auditing, data management, and workflow, and proficiency management. It manages the availability, integrity, and security of the data used in the organization.
Data storage is scalable, cost-effective, and allows fast access to data exploration while supporting it in various formats.
A data lake is enhanced with security in its every layer, starting with authentication with storage to consumption for stopping the unauthorized users from access.
Data quality is an essential part of a data lake, which is used to exact business value to avoid poor quality data, which can lead to poor insights.
Data discovery is an essential stage before you start preparing the analysis. The data is organized with a tagging technique.
It helps to evaluate the risk factor and tracks the changes in essential data set elements while capturing who and how changed it.
This component makes error corrections in data analytics by dealing with the origin and processing It to destination.
This is the most primary stage of data analysis, which helps in identifying the right dataset before starting the exploration.
Benefits of a data lake
Data lake works on the principle of schema-on-read, which means there is no predefined data that needs to be fitted before storage. It can only be done when the data is read, processed, and adapted into schema according to the neds. This procedure helps to save a lot of time for data scientists. They can access, prepare, and analyze the data with accuracy for a variety of causes. The data lake is also associated with Hadoop oriented storage where the data is loaded into it, and business analytics and data mining tools are applied.
The data warehouse and data lake are used together and start working as a union by playing their role in analytics. In the maturity stage, these data are added into data lake by adopting governance and management. It improves the ability of transformation and advanced analytics. It also offers scalability and reduces long term cost of ownership. The main advantage is that it quickly adapts to changes and is the centralization of different content sources. Users around the globe from various departments can flexibly access the data.
Difference between?Data lake and data warehouse
Data lake and data warehouses are similar concepts due to their common purpose and objective, which makes confusion amongst many people. They both are used as storage depositories in an organization for creating a one-stop data store for various applicants.
Schema-on-read vs. schema-on-write The data warehouse is defined and structured before the storage, whereas the data lake allows data to store in native format due to no predefined schema. This means the data warehouse prepares its data before processing, but the data lake does it later.
Complex vs. simple user accessibility A data ake needs an expert to organize the data in the simplified form before storage. In contrast, in the data warehouse, the information is easily accessible to tech and non-tech users with the help of its well defined documented schema.
Flexibility vs. rigidity Data warehouse takes time to define the schema and considerable resources to modify it when required in the future. Still, the data lake can adapt to change quickly with the increase in storage capacity to scale the server.
Risk of Using Data Lake:
With lots of advantages of using data lake, there also exhibits some risk factors. The data lake may lose relevance or momentum after some time, and designing the data lake involves an even more tremendous amount of risk. The unstructured data leads to ungoverned chaos and the need for sophisticated tools, which increases the storage and computational cost. The data lake doesn’t provide an option for finding previous analysts who have worked on the data due to no presence of lineage. The most significant risk factor is security and access control because even after the privacy and regulatory need, some data can be put without any insight.