Today most of the data that is available on the internet is unstructured and hence getting value out of it becomes challenging. Data that is required by analysts and ML engineers should be in an easily digestible format to generate insights on it and to train ML models. But the data that can be accessed from the internet is mostly in texts such as tweets or blogs, randomized table structures, and complicated formats which are inappropriate for all the data users to use. 

What is Data Engineering? 

 

Data Engineering deals with solving the problems that arrive at the most initial stages of any data science related task i.e., processing the raw data. The raw data that arrives in the data lakes and warehouses has all sorts of discrepancies that are almost unusable to anyone from the Data Analysts to the Data Scientists. Hence, the data needs to be engineered in such a way that it becomes suitable for the entire data science pipeline. 

How does Data Engineering help? 

Data Engineering allows the data to be pre-processed and transformed into useful formats so that it aligns with the business needs and actionable insights can be drawn from them. The raw data needs to be put into proper structure and the missing values need to be dealt with. The data should also be free from any errors such as alphabets within a column of numbers or having a mismatched data type. Data Engineering solves this and beyond that, it helps build data pipelines and architectures which can do the data cleaning and processing tasks automatically. 

What does a Data Engineer do? 

Data engineers are an essential part of any organization dealing with massive amounts of data.  They help build, test, and maintain data pipelines and manage the entire data architecture to ensure that the data flow is smooth. They are responsible for cleaning and transforming the data and also making sure that the processed data meets the company standards. 

By collaborating with stakeholders and the business managers, data engineers decide the appropriate steps and precautions to be taken on the data before building such data engineering pipelines so that they provide the most value to the data handlers down the line. They also ensure that the data pipeline is transparent and compliant with the data governance policies. Security of the data is also an important task that a data engineer has to keep an eye on. 

 

As data engineers are experts at making data ready for consumption, to do so they incorporate techniques such as ETL (Extract Transfer and Load) and also ELT (Extract Load and Transform). ETL is used for gathering data from multiple resources and data repositories into a single structure, and processing it such that it can be used further for analysis and proper storage. It helps create a system where the data is consistent everywhere and where it can be modified and updated as required. 

The extraction process usually involves combining data from various sources into a single data frame. This data is again corrected and modified to fit under certain standards and the invalid data is removed. The various sources for the data could be ERP (Enterprise Resource Planning) data from SAP, CRM (Customer Relationship Management) data, data warehouses of the company, data from smart sensors, or any data from the internet which are then converted into an easily digestible format such as JSON, CSV, XML, etc. 

During the transformation stage, data is modified and rebuilt into a structure that is suitable for analytics and the data science pipeline. The data is cleansed by removing any discrepancies such as trivial formatting errors and data type mismatches. The data is also augmented so that it is suitable and functional for the other data users down the pipeline. Various standardization tricks are also applied here to keep the data consistent and normalized. 

In the loading stage, the data is securely transferred with other data users that can analyze it to produce insights and/or can train ML models using it. This process makes the data business-ready and provides instant data access to anyone within the organization or some other external party. 

ELT is used where the load times should be shorter such as in a stock trading analysis or scientific studies. The load time for ELT is shorter because the process has the transform component at the end reducing the overall time. ELT solutions for business intelligence systems always come from a need to quickly load unstructured data. Now in ETL, the data is first transformed and then loaded which makes ETL a more time-consuming process. But ETL delivers data that is more refined and transformed from the onset and it is only used where periodic updates of information are required, instead of real-time updates making it more conducive for advanced analytics. 

Currently, various ETL and ELT tools are available in the market that provides and equip an enterprise with proper data engineering solutions and allow such entities to create, store, transform and govern the data and help them stay above the competition. 

 

Data Engineering With ANAI 

With ANAI, businesses can deal with the difficult task of maintaining huge data pipelines and can easily cleanse, transform and make the data appropriate for analysis and ML models. ANAI also enables organizations to modernize their data pipelines to achieve the speed and scalability of their business needs. 

The features that ANAI offer regarding data engineering are: 

• More than 100 data connectors for easier data ingestion. 
• Conduct pre-analysis on the data. 
• Machine leaning based data wrangling. 
• Generate insights easily using ANAI’s data visualization tools. 
• Do Feature Engineering on your data. 
• Detect and correct anomalies. 
• Get useful recommendations based on your data quality. 

Automated Data Engineering Pipeline, Data Engineering Explained, Low Code No Code Data Engineering,

For more information on how your organization can build and execute an effective data engineering solution, explore ANAI’s offerings and contact us here for more details or visit www.anai.io.