Data Pipelines
vZ Data Pipeline can ingest data from multiple disparate data sources and push into destination stores on the cloud. Ours is a wizard based simple and no-code approach ideally suited for users with minimum exposure to ETLs and programming.
Why choose us?
Using our pipelines, large datasets can be ingested through data streaming. Pipelines can be scaled using distributed computing and by leveraging containerization in a distributed setup.
Data Preparation
Data preparation is the first step in the pipeline. It provides a self-service facility to the user to connect to various data sources, explore and prepare the data for further processing
Data Processing
Our intuitive UI makes configuration of processing steps simple and easy. Data cleansing, type conversions, binning, splitting to multiple rows, excel type expressions can be added as steps in data processing
Data Finalization
Data is pushed into datastores (such as Elastic Search, Postgres etc.) in the data finalization stage. A trial run is allowed. Upon successful completion and validation of the trial run, the data can be pushed into the data stores and data pipelines can be scheduled
How much latency do we need?
Modern businesses will be built on real time data. More and more functions are adapting to real time data for decision making. Latency in using the data can severely affect the business landscape of an organization - newer competitors armed with real time data could disrupt existing business with faster and agile decision making, acquire customers with superior understanding of customer needs; by ensuring faster and contextual engagement. Generation of more and more digital data and at a very fast pace, easy to use modern infrastructure on the cloud and a connected environment (ubiquity of smart devices) will only make old data less and less useful and increase the demand for the most recent data.
Traditional ETLs ingesting data in batches and schedules
Can’t provide instant insights
Batches cutting across business operations have to be scheduled such that enough lag is created for completion of dependent batches and activities. Modern pipelines are aware of the events and can trigger the next set of responses.
Find it hard to finish within a dedicated time window
Batches will get fatter and fatter as more and more digital data gets generated. Modern pipelines can consume data as soon as it is generated.
vZ pipelines are modern pipelines that allow you to continuously stream data. This will reduce the latency and give almost real time visualizations and forecasts.
Data Governance
Today, there is a growing demand within organizations for cross-functional and siloed data to be made available. Users are having access to and analysing more data than ever. This means that data, including personal information, can be seen and read by many bringing to the fore the importance of data governance. Challenges increase manifold when data is big, users want to work collaboratively, data is obtained from a variety of platforms and in different forms - structured, unstructured and semi-structured. Not only should the data be made available and accessible, they have to be relatable and misuse has to be prevented.
vZ Pipeline provides options for data governance while ensuring that data becomes more freely available and accessible. Data governance options on our platform help organizations to continuously improve the quality of the data and control its usage by the stakeholders. Various options to improve quality of the source data reduces the effort in processing and analysing the data, allows historical data to be analysed and increases accuracy. vZ Pipeline allows finer access control on the dataset and the data. Such controls protect organizations against unintended access and use.
Data to Insights
The purpose of using data is to derive meaningful insights out of it – insights that help an organization to stay ahead of competition, identify customer trends, increase efficiency, reduce costs and so on. Such insights can be obtained by visual observations, applied mathematics, statistics, predictive modelling and machine learning. Data pipelines are first step towards gathering and preparing the data for analysis.
Unlike many other products, vZ Analytics platform comes with the entire suite of data pipelines, visualization and AI / ML thereby facilitating data to insights on the same platform.
Key features
vZ Pipeline has all the features that any modern data pipeline solution should provide - wide range of connectors to injest data from multiple sources, easy to configure cleansing and transformation operations, choice of batch or stream based pipelines, data stores on the cloud and a highly scalable architecture
Off the shelf connectors are available to connect to popular databases such as MySQL, Oracle, Postgres, MongoDB etc. Large sized data files can be uploaded by connecting and reading from sFTP servers. CSV and excel files can be uploaded directly from the Desktop. Our list of connectors is increasing by the day
Connnectors
Simple and secure
Our data pipelines let you draw on the flexibility and instant elasticity of the cloud. Pipelines can be quickly deployed on the cloud and compute resources can be scaled up and down as necessary, enabling a faster response at a lower cost. With data on the cloud, also get faster visualizations, analytics and insights from AI / ML products
Elastic and Agile
Go for the cloud
Pipelines can be run on demand, continuously or at scheduled intervals. Data can be inserted or updated based on the primary key; data can be appended to an existing dataset. Run statistics such as run type, execution time, number of records extracted, processed and uploaded etc. are available for every run
Runs and history
Monitor anywhere, anytime
Our pipelines can push the data into one or more destination data stores. User can choose the destination data store depending upon the need and volume of data. Currently data can be pushed into data stores like Postgres and Elastic Search. Visualizations, AI / ML are supported on all the destination data stores
Destination data stores
Users get to decide
Pipelines and data sets are versioned. Pipeline versions keep track of the changes in the preparation, processing and finalization steps. Data changes due to changes in the pipeline are also versioned. Versioning facilitates comparisons and rollbacks. In AI / ML ops, versions tie the model and the dataset on which it is created
