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Cloud Integration - Apr 28 2021

Should My Business Use Open-Source Data Integration Tools?

Yes, your business should consider open-source data tools. Today, we live in a world of big data and an explosion of cloud-based applications. Yet, there are thousands of companies that use legacy systems for data integration. It has remained a fixture in enterprise technology environments. Here is a quick guide to enable seamless transformation.

What is a landscape assessment?

A landscape assessment is the process of identifying the underlying data sources, variety of data and data volumes for a planned data migration or integration project. The landscape assessment is key to building an efficient solution. As mentioned earlier, there are many applications rendering both structured and unstructured data from cloud data warehouses, NoSQL databases, and traditional structured data sources like Oracle and SQL Server.

An initial step in this assessment is to list every assumption and anticipated overhead to your company’s data landscape to get a clear picture of these multidimensional data sources, the compute platform, and data volumes.
Once you identify the ever-growing need for a change in the data landscape, it’s often clear that the transformation will require a clean slate.

How to Determine the Right Data Analytics Tech Stack

Identify the Key Data Marts that need to be transferred to a different data landscape. This enables each department to isolate the use, manipulation, and development of their data.

Leverage distributed data processing engines like Spark to process large data volumes by splitting the work up into chunks and assigning them to computational resources. Some of the alternatives include Apache Hadoop, Google Big Query, Elasticsearch, and Presto.

Can we eliminate legacy-system ETL jobs by switching to open-source tools?

Eliminate legacy system-based ETL Jobs by switching to robust open-source tools like Python or Scala. These provide an elegant, versatile language with an ecosystem of powerful modules and code libraries. Writing Python/ Scala code for ETL starts with knowledge of the relevant frameworks and libraries, such as workflow management utilities, libraries for accessing and extracting data, and fully featured ETL toolkits.

Size and costs are key factors to consider when aiming for migration of such a vast scale. Licensed tools usually come with a higher hard cost. This includes just the base fee for the software, integration, services, and annual licensing fees. With open-source frameworks, hard costs can be reduced by paying only for additional infrastructure, security, and storage costs as needed.

What outcomes should we expect?

With an effective implementation and strategic use of resources, your company can expect to see gains almost immediately.

Facilitate Performance

Open-source Data Analytics tools ensure business users better and faster access to a larger amount of processed and integrated data to inform their decision making.

Enable Advanced Data Profiling

Business intelligence, machine learning, and other data-driven initiatives are only as good as the data that informs them. Open-source tools with the right process engines ensure solid data management.

Handle Big Data

Open-source tools can be used to combine large data sets of both structured and unstructured data from disparate sources in a single mapping using Hadoop or similar connectors. These engines ensure more extensive data processing capabilities.

Have questions? We help companies like yours, every day.

Email us at hello@nextphase.ai

 

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About NextPhase.ai

NextPhase.ai is a cloud data management and analytics services provider. For 10 years, we’ve helped global companies harness the power to turn data into insights that drive growth. Whether you’re migrating to the cloud or implementing a cloud data warehouse, contact us to schedule a workshop.

 

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