Every day, data has grown and evolved; every fundamental of any aspect has, and needs data. With data, a human + technology can drive decision-making, innovation, and efficiency across various fields. Data comes from many sources and data also exists all around us, but to collect and organize, data can be too much to handle. Before data can be useful, there is raw data that needs to be processed to make the data able to use, analyze, or make reports from it.
Years ago, there was an ETL method that nowadays has been considered a traditional method, even though it used to be the most reliable method, but the process of the ETL method has become a challenge in today’s fast-paced, data-driven technology era. The ETL method works by gathering data from various sources (extract), organizing the data, transforming it into the same or consistent format (transform), including cleansing the data, validating and authenticating the data, etc., and finally loading it into specific locations (load), which could be into a data lake, a data warehouse, etc.
Zero ETL on hands
Simply put, zero ETL is an approach to integrating data without going through the traditional ETL process.
The ETL method and non-ETL method have different approaches to manage data and integrate data. Few common problems or challenges when doing extract, transform and loads are:
- Data movement or data storage, over movement and storage places affecting many things such as time consuming or cost consuming
- Data integration, with ETL method, data integration have to through many steps
- Data latency, data might be still generated or updated in the source system only, then need to re-do for it available in the target system
But with non-ETL are:
Accessible with minimal coding required
The obstacle to adapt with every query language is unnecessary, one query language can lead and finalize any analysis.
Easy approach
Simply pick the work space on desktop or on cloud, choose the package plan, install and integrate the database with software, input the details connection (host, port, database name, and authentication), make the virtual data and choose the data, and do the query for the analysis and report.
Better cost than ETL options
Cutting down many steps (ETL) into few steps (Dave) is surely affecting the cost effectiveness and efficiency. Dave is able to avoid any unnecessary process and cost, making it more profitable for any user.
Real-time data processing/availability
Able to process uncleaning, unstructured, unorganized, raw data. No need to extract data to become anything, less time consuming, and very importantly the processed data can give faster results and insight to make analysis or report.
Simplified Architecture
Processing data with data virtualization. Moving and transforming data are unnecessary steps. With data virtualization will make the process focus on direct, seamless, and real-time data integration.
What does ETL method and Dave look like in real use cases?
Let’s make a simple comparison of one case with two methods, the one with 5 steps to go and the other is simply 3 steps to get down to the analysis.
Case Study
Integrate data with Dave: simple, flexible, cost effective
Motivation: Combining and analyzing data from various sources (CSV, PostgreSQL, MySQL, Oracle) for timely reporting.
Prior data situation:
- Differences in database systems
- Varying data readiness times
- The need to use different programming languages
- Limited programming language skills
- Limited time
Problem:
A civil servant has few tasks and needs to be ready at the same time. Although the task has the same method to get done, it should be processed one by one.
The civil servant is good at using PostgreSQL. The data he needs to process is available in a MySQL database, so the data needs to be dumped first into CSV format; this dumping itself took most of the time before the deadline.
Another task is to make an analysis from the data on the Oracle database.
Dave as solution:
Integrate all of the source systems, direct connections, and easy virtualization of data (no ETL needed) by using Dave.
Benefits:
- Cost saving
- Reduce ETL running time and lag time
- Finish analysis in a shorter period
Comparison
Without Dave
With Dave
Approach
The analyst/civil servant needs to wait for the data to be ready before he can do analysis
The analyst/civil servant just needs to integrate the database (no need to dump any data) and make virtual data
Setup Time
Weeks to set up ETL and data availability
Hours to install Dave and link the databases with the credential
Query Writing Effort
The analyst should learn a new programming language in a short time
Standard MySQL query language is enough; the analyst also does not need to learn a new query language
After all, the real deal of zero-ETL is processing and getting the data without complicated data integration, delayed data availability, or high maintenance costs. This innovation with simplification will lead the way of how data is processed, once again, in this fast-paced data-driven era.
Read more about Data Virtualization and Dave for better insight.
Also, use Dave for better solutions.
