Improving data modeling and workflow in hadoop

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Improving data modeling and workflow in hadoop

Data work flow modeling in hadoop framework

Hadoop is well known for its schema on read approach that simply denotes to the raw and unprocessed data that is loaded into hadoop distributed file system with schema defined at the time of processing the data and not having a functionality of a pre-defined schema. There is another term that is coined for hadoop and that is schema on write that can be used in conjunction with traditional data management systems. Hadoop uses schema on write which has the flexibility of making a schema be defined in real time. However, if we are using schema on write with traditional data management systems then a predefined schema needs to be uploaded before it has to store the data and for this we need a long and lengthy process of data analysis, data flow and data modeling; and in some cases if we made a wrong predictive decision then we again have to process the flow cycle. For this we need a data modeling technique that will help us in improving the overall performance for hadoop and big data analysis. This article focuses on some methods to improve the data modeling and workflow structure in order to make hadoop a suitable environment for structured as well as unstructured data.

What is data modeling and why it is useful for hadoop framework?

Data modeling is a useful technique to manage a workflow for various entities and for making a sequential workflow in order to have a successful completion of a task. Talking about hadoop and its big data model, we need to have a comprehensive study before implementing any execution task and setting up any progressive environment. For that we have a dataflow sequence and big data model. Mainly hadoop is a collection of tools and techniques as it is not a single technology, so at each point of time we need a task execution environment and some projection plans as well. The data modeling and logical workflow consists of an abstract layer that is used in management of data storage when the data is stored in physical drives in hadoop distributed file system. Because of huge expansion of data in terms of big data, we need to have a multi distributed and logically managed system. The data modeling also helps us in managing various data resources and creates basic data layered architecture in order to optimize data reuse and execution failure as well. Data flow also helps in managing the schema on read and schema on write architecture.

Improving data modeling with some improved modeling techniques

Improving data modeling with some improved modeling techniques

Hybrid model (data flow and logical data management model)

Apache oozie are inbuilt tools for managing map reduce and to make sure they are synchronized in order to maintain equilibrium amongst the tasks that are assigned by job trackers to task trackers. However, we still need a modeling scheme to manage and maintain a workflow of hadoop framework and for this we need a hybrid model for more flexibility. In spite of many NoSQL databases that are used to resolve the problem of data management for schema on read and schema on write, we still need a hybrid model in order to improve the overall performance for SQL and NoSQL databases. As big data is changing a lot in terms of its execution approach, we need to have a new data and storage model (two separate models). We can fix these problems by using the data migration technique in order to migrate big data (raw and unstructured data) into NoSQL data.

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Physical data awareness model

The traditional approach often indicates the structure first, collect rest approach as the first and foremost prime approach towards data architects and this goes for structuring the data, maintaining a data structure first and then allowing the data to enter the system. This approach fails to perform in terms of unstructured data and we need a dynamic scheme that defines a schema as the data changes and would be well suited in terms of big data. This methodology is best suited to define an existing schema on and once the data is stored and collected and we must find a way to apply a schema function and various other versions of data that can work over this schema. This methodology has a trilogy of data block, data version and data relationship, which simply means that data may have different forms and can form different relationships amongst each other and it can have different data blocks (a physical data defined with metadata). In order to know what model should be used, we need to have a layering approach so that a computing program knows where to retrieve the data from.

“Data Structure” data model

In order to meet business requirements, we need to have a data model so that we can analyze the relationships amongst the data. This model mainly consist of HCatalog repository and being a part of data model it needs to identify the relationships, for which we need to define a data structure model.
Here are some key points that a DSM (data structure model) must have-:
• It should be able to generate HCatalog file definitions
• It should represent data source models as an enterprise model
• It should contain some data source definitions.

“Data Structure” data model

Conclusion
This article thoroughly describes many of the data modeling techniques that can be used within the existing hadoop framework. Moreover, to improve the overall functionality of the framework we need to serialize the data and make the data suitable enough to be used by enterprise applications to ensure that hadoop is more reliable for data resources and making things valuable. We have discussed some new approaches which can be incorporated with data modeling and can also help the apache oozie framework. Better flow management and dynamic data techniques will be an innovation for futuristic approach to make hadoop a better place.

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