ETL and Data Warehousing

ETL and Data Warehousing

Transforming data at scale and speed to deliver actionable business insights

IT teams managing large scale data analytics projects often grapple with collation and processing of huge volumes of data from diverse sources. Our data integration, migration and ETL experts have extensive experience in planning, creating, and implementing comprehensive data warehousing solutions across industries. We ensure that every step of the ETL and EDW process is customized to specific business needs and deliver the desired outcomes assuring that the extracted data is transformed for successful loading into the data warehouse.

Different components of ETL

Pull data from different sources

In this step, we connect to different data sources and extract the necessary data. This extracted data should be made available at the earliest for further processing before analytics.

Clean it to get accurate, consistent and good quality data

This process involves detecting any errors, redundancies, or inconsistencies in the data. It helps to get accurate and consistent data to maintain good quality in the data warehouse.

Prepare and transform the collected source data into a form that matches the target system requirement

This step helps in transforming the extracted and cleansed data into a form that can be used for analysis. Pre-aggregation might help boost the performance, but at the downside of increased cost.

Import the transformed data into target/warehouse

The transformed data is then imported into the target database or warehouse, either incrementally added at regular intervals or at one go, depending on the business requirement of the customer.

Design and manage a strong ETL architecture with recovery settings

To effectively implement an ETL architecture, it’s necessary to regularly streamline and audit the entire process of data collection and processing, to minimize errors and enhance efficiency.

A system designed to deliver

Query Performance

We ensure sub-5 seconds query response times even while processing hundreds of terabytes of data. This enables you to run analytics at the speed of thought!

Zero Pre – Aggregation

We don’t rely on pre-aggregation or data cubing to guarantee query performance. This means you can drill-down to any levels of granularity for root cause analysis

Data Optimization

Through intelligent query processing and data management, we handle different formats and sources to serve queries faster, leading to optimal performance

Live Data Refresh

We have the capability to ingest extremely high volumes of data across different and diverse sources. You can run your analysis on fresh data and act in real-time!

Success Story

Processed huge volumes of customer and POS data, generating customer insights within seconds
250 TB+ data | 60% faster reporting
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Experience Data Exploration at Scale and Speed

Our analytical engine, powered by Apache Spark, has the capability to query and analyze 100s of terabytes of data within seconds, using extremely low hardware requirements. Besides remarkable performance on a variety of database workloads, it also provides several features designed to offer better performance, scalability, reliability and ease of use. These features include local caching, adaptive query and index cache, scale-out architecture, fully fault tolerant, easy and flexible deployment on cluster, among others.

Intelligent Data Storage

Data is stored in a columnar data format in HDFS, in contrast to conventional row-oriented databases. This minimizes the amount of data accessed by each query and avoids reading unnecessary columns. Moreover, it also provides a number of compression and encoding techniques which reduces the overall storage footprint, and dramatically improves query performance by reducing CPU, memory, and disk I/O requirements at processing time. The original data size is generally reduced by 90% with zero data loss even with high availability redundancy turned on.

Intelligent Data Processing

The system generates a unique mix of specific materialized views (for frequently accessed data) as well as primary indexes (reverse indexes, bloom filters, range partitioning) on the ingested raw data, storing them as segments distributed across the cluster. Only the index segments/views that are required by a query are targeted, thus reducing the data volume that needs to be processed. Since fewer CPU cores are required to process each query, the unused computing resources are made available for other queries so that they can run in parallel.

Efficient ETL Pipelines help to achieve

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