Modern ETL

Alooma is an enterprise ETL platform with state-of-the-art data integration in the cloud: extract, transform, stream, load, connect, and visualize all your data, error free.

More than an ETL solution

Other companies have ETL tools. Alooma is a scalable, secure, enterprise ETL platform in the cloud.
Extract your data

Extract your data

We support a vast number of native integrations across databases, SaaS applications, on-premise and cloud storage, APIs and SDKs, and many other custom sources.
Transform your data

Transform your data

Our Code Engine lets you transform and customize your data any way you want. Our Mapper provides automatic OneClick and custom mappings, whether your data is structured or semi-structured.
Stream your data

Stream your data

Pumping large volumes of data at scale to a data warehouse is difficult. Our pipeline can reliably handle billions of events per day with millisecond latency.
Load your data

Load your data

Alooma supports a wide variety of destinations for your data, including Amazon Redshift, Google BigQuery, Snowflake, and more.
Connect to your data

Connect to your data

Want custom mashups from multiple input data sources? We make it easy. Trying to combine your in-cloud and on-prem data storage silos into one location? We've got you covered.
Visualize your data

Visualize your data

Alooma Live puts your entire data stream at your fingertips. You can collect live data samples, filter your stream on the fly, monitor data behavior, debug incorrect data events, and measure real-time metrics, with everything coming together to help you identify patterns as they form.

ETL FAQ

What is ETL?

ETL is generally understood by many to be the process of translating the data from one format or store to another.

ETL stands for "Extract, Transform, Load", and is the common paradigm by which data from multiple systems — typically developed and supported by different vendors, departments or stakeholders — is combined to a single database, data store, or warehouse for legacy storage or analytics.

Extraction is the process by which data is extracted from various data sources. Transformation involves transforming the data for storage in proper format for query and analysis. Finally, loading occurs when the transformed data is loaded into the target database, data store, data mart, or warehouse. Learn more about ETL

What is the traditional ETL process?

Traditional ETL tools are typically homespun, on-premise and support batch processing. Historically, teams would run nightly ETL and data consolidation jobs using free compute resources during off-hours.

These tools typically come with a number of shortcomings. First, their homespun nature means an organization must absorb the cost of maintaining their own data engineering team and knowledge is lost when team members leave or code (or configuration) goes undocumented. Since a homemade ETL solution is a one-off, standard best practices as well as security and scalability planning may be underserved, and the pipeline is only as good as the team which implemented it.

The on-premise nature of traditional ETLs comes at the cost of vertical and horizontal scaling, downtime loss, and even power consumption and facilities costs.

Additionally, an ETL's batch-processing nature means that updates to the data set (and related insights) only appear periodically. Batch processing can also go wrong; it's less costly to troubleshoot a small number of records in real time than contend with the loss of time associated with losing an entire day's worth of ingested data.

What is the modern ETL process?

While the traditional ETL process includes extract, transform, and load, there is more to it than that.

In that process, data was extracted, in batch, from an OLTP database, and transformed in a staging area for consumption by BI teams. But the modern process can be much more complicated.

These days, data ingestion must work in real time, so users can run queries and see the present picture at any time. Can the ETL handle the full variety of data sources and streams, with new ones being added all the time?

ETLs must be fault-tolerant, secure, scalable, and accurate — along the entire pipeline — with the ability to configure error messages, reroute faulty events, and enrich data programmatically on the fly.

With modern cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake, you can perform transformations directly on massive datasets without the need for a dedicated staging area.

Why choose a modern ETL tool?

Doing your ETL in batches makes sense when you do not need your data in real time. While many companies have disparate and complicated warehousing systems, incompatibility between systems and knowledge lost due to turnover results in spiraling costs and time required to consolidate data.

Modern ETL tools like Alooma are cloud-based, fully managed, and support batch as well as real-time data ingestion. Alooma's enterprise platform provides a format-agnostic, streaming data pipeline to simplify and enable real-time data processing, transformation, analytics, and business intelligence.


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