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A Serverless ETL Service

Get more value from your data with Tiger Analytics’ partnership with AWS

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Businesses now rely on data for analytics, machine learning, decision making, etc. Data is stored and analyzed using a wide range of sources, such as data lakes, data warehouses, etc. The data needs to be processed first before it is loaded into these sources. These processes encompass data discovery, extraction, cleaning, and preparation, among others.

AWS Glue is a serverless ETL service that deals with data discovery, data transformation, and data preparation so it can be effectively utilized by other services. In order to assist with data integration, AWS Glue provides both visual and code-based interfaces. The AWS Glue Data Catalog enables users to quickly search for and retrieve data. With a few clicks in AWS Glue Studio, ETL developers can graphically construct, run, and monitor ETL operations.

With more than 150+ certified AWS experts, Tiger Analytics has successfully implemented AWS Glue along with other AWS services for customers across different industry verticals.

Businesses now rely on data for analytics, machine learning, decision making, etc. Data is stored and analyzed using a wide range of sources, such as data lakes, data warehouses, etc. The data needs to be processed first before it is loaded into these sources. These processes encompass data discovery, extraction, cleaning, and preparation, among others.

AWS Glue is a serverless ETL service that deals with data discovery, data transformation, and data preparation so it can be effectively utilized by other services. In order to assist with data integration, AWS Glue provides both visual and code-based interfaces. The AWS Glue Data Catalog enables users to quickly search for and retrieve data. With a few clicks in AWS Glue Studio, ETL developers can graphically construct, run, and monitor ETL operations.

With more than 150+ certified AWS experts, Tiger Analytics has successfully implemented AWS Glue along with other AWS services for customers across different industry verticals.

Features of AWS Glue
Scalability Based On The Workloads
Auto Scaling is a serverless feature of Glue that supports upscaling/downscaling based on the workloads. As the job progresses and goes through advanced transforms, AWS Glue adds and removes resources depending on how much it can split the workload. One can handle over-provisioning resources, optimizing the number of workers, or paying for idle resources.
Cost Effective
Zero cost as there is no infrastructure to set up or manage.
Consistent Data Management
Glue supports three open-source frameworks, including Apache Hudi, Apache Iceberg, and Linux Foundation Delta Lake, helping in consistent data management.
Reliable Data Security
AWS Glue Sensitive Data Detection streamlines the identification and masking of sensitive data, including personally identifiable information (PII) such as name, SSN, address, email, etc.
Our Capabilities
01
Event Driven ETL Pipeline

Get better real-time data processing, allowing for faster decision-making and more efficient resource utilization

02
Data Preparation and Exploration

Outcomes of data exploration can be a powerful factor in understanding the structure of data, values distributions, and interrelationships

03
Data Lake Ingestion

Get the benefit of faster speed, scalability and efficiency

04
Data Cataloging

Utilize our service for improved data efficiency, reduced risk of error and improved data analysis

Our Accelerators Set Us Apart

  • Data Quality Framework
  • Data Lake Management
  • Data Fabric

Data Quality Framework

strength
Great Expectations – Data Quality Framework

Open-Source framework for Data Quality. It is highly configurable with table & field level rules, integrated with Airflow and monitoring tools.

Data Lake Management

strength
ETL Modernization

New-gen ETL tools on cloud like AWS Glue provide a complete integration ecosystem enabling customers to build scalable lakes and lake houses for data analysis, process petabytes of data in batch and real-time using Apache Spark and migrate from expensive traditional ETL solutions to gain flexibility and reduce costs.

Data Fabric

strength
AWS Data Fabric

Enable faster time-to-value for business users, higher productivity for data engineering and operations, and greater governance and compliance fidelity. With a data fabric built, one can hyper-automate data discovery, data governance, and data consumption on AWS.

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