Datametica Solutions

Datametica streamlines cloud migration through our advanced automation techniques. We facilitate a swift and cost-effective approach using our distinctive toolset and solutions, alleviating frustration and anxiety for businesses of all sizes.

[datameticasols]

Modern enterprises face rising cloud costs, performance bottlenecks, and strict security demands. Effective azure migration planning helps organizations address these challenges through structured assessment, optimized architecture, and governance-driven cloud operations. Datametica supports enterprises with end-to-end Azure optimization, from gap analysis and security evaluation to long-term managed cloud support. Powered by Datametica’s azure data migration tool, organizations gain better visibility, improved performance, and controlled cloud spend. This infographic highlights how a structured Azure modernization strategy can enhance cloud governance, security, and scalability while supporting business growth.

[datameticasols]

American Sports Goods Retailer Successfully Migrates from Netezza to GCP

Learn about how an American sports goods retailer made the move from Netezza to GCP, and the benefits they experienced as a result.

Enterprises moving away from legacy platforms like Netezza need a faster, low-risk approach to google cloud migration. In this blog, Datametica explains how organizations can achieve an accelerated Netezza to GCP migration using automation, structured assessment, and proven migration accelerators. The article highlights how the right strategy simplifies workload conversion, ensures accurate data validation, and minimizes business disruption—helping enterprises modernize analytics environments and complete google cloud migration with confidence.

[datameticasols]

5 Mistakes to Avoid While Migrating from Teradata | Datametica

Migration of Teradata is a huge challenge. That is why we have compiled a list of migration mistakes to avoid in order to efficiently migrat

Migrating from Teradata is a major transformation initiative for organizations seeking modern analytics platforms. Whether the objective is a Teradata to BigQuery migration or a Teradata to Snowflake migration, the success of the journey depends heavily on planning, execution, and validation.

One of the most common mistakes organizations make is rushing the migration process without fully understanding the complexity of the Teradata enterprise data warehouse. Attempting to move all workloads at once often leads to disruptions, failures, and extended timelines. Equally risky is migrating poorly defined or unclean source data, which can result in compatibility issues and inaccurate analytics in the target environment.

Another frequent challenge arises when business users are excluded from the migration process. While IT teams focus on data integrity and performance, business teams rely on applications and reports that may appear obsolete but remain critical. Lack of collaboration can lead to post-migration issues, support overload, and reduced user confidence. Additionally, relying on manual or improper tools to assess, translate, and migrate Teradata workloads significantly increases effort, cost, and error rates.

Many organizations also assume migration is complete once the data is moved to the target platform. Without thorough testing and validation, discrepancies between source and target systems can go unnoticed, impacting reporting accuracy and business decisions. Proper validation ensures that data functions as expected after migration.

With proven experience in both Teradata to BigQuery migration and Teradata to Snowflake migration, Datametica helps organizations avoid these pitfalls through automation-driven assessment, code conversion, and data validation. The result is a secure, efficient, and reliable migration that accelerates cloud adoption and delivers measurable business value.

[datameticasols]

Automated migration support with datametica raven

Enterprises modernizing their data ecosystems often struggle with the complexity of converting legacy SQL, ETL workflows, and scripts during cloud adoption. This is why many teams are now turning to datametica raven, an automated transpiler designed to simplify and accelerate large-scale cloud migration. Traditional manual conversion techniques are slow, error-prone, and require deep legacy expertise. Raven addresses these challenges by automating the translation of workloads from systems such as Oracle, Teradata, Netezza, and other on-prem environments.

During a raven migration, code is converted into cloud-native formats compatible with platforms like Google BigQuery, Snowflake, Azure Synapse, Databricks, and AWS Redshift. What makes this especially valuable is Raven’s ability to maintain uniformity and accuracy across scripts of different lengths and complexity. This ensures a reliable foundation for cloud analytics without forcing teams to rewrite logic manually.

One of the major strengths of datametica raven is its focus on precision. It identifies inconsistencies, preserves logic, and produces consistently formatted code that is easier to review, maintain, and optimize. This significantly reduces migration risk and minimizes the need for multiple revision cycles. By removing repetitive manual tasks, organizations also shorten project timelines and free their teams to focus on higher-value modernization efforts.

A raven migration also supports downstream modernization by improving script structure and readability, which helps accelerate validation and testing phases. This results in smoother cloud adoption and faster readiness for analytics, AI, and governance initiatives.

For enterprises moving toward cloud-native architectures, automating code conversion can be the difference between long migration delays and a streamlined transition. Tools like datametica raven offer a secure and scalable way to convert legacy workloads while maintaining accuracy, performance, and long-term sustainability. This approach helps organizations modernize confidently and build robust cloud environments for future growth.

[datameticasols]

Why general data validation fails in complex cloud migration projects

A persistent challenge in complex cloud migration projects is achieving high-quality data validation, especially when using a "lift and shift" approach. the core issue is that improving data integrity often requires intentional format changes, such as converting date to datetime or adjusting scale and precision for float values. a standard data validation tool will flag these intentional changes as discrepancies, creating false positives and dramatically slowing down the project timeline.

for instance, if a source date of 05/08/2023 in mm/dd/yyyy format is stored in the target system as 2023-05-08 13:10:10 (a yyyy-mm-dd hh:mm:ss timestamp), a simple comparison fails. this is where the power of an automated data validation tool with intelligent capabilities becomes apparent. solutions like pelican utilize a sophisticated rules engine to understand and validate these format transformations.

this intelligence validates the comparison based on predefined or learned rules, ensuring that data integrity improvements are not mistakenly flagged as mismatches. for organizations focused on modernizing their data platform, adopting a validation process that acknowledges these intended changes is crucial for accuracy and speed. relying solely on basic comparisons introduces unnecessary risk and delay. choosing the right tooling ensures your migration is secure and efficient.

[datameticasols]

Building Data Lake for Modernizing Your Business | Datametica

Data lake on cloud helps organizations modernize their data platform. Building a data lake involves various steps like data lake architectur

A well-structured data lake is the backbone of modern enterprises, enabling scalability, flexibility, and advanced analytics. To build an efficient data lake, businesses must first understand their legacy system’s data access patterns and establish a strong data foundation on cloud platforms like Google Cloud, Azure, or AWS. Using Datametica’s intelligent automation tools—Eagle for data discovery, Raven for automated workload conversion, Pelican for data validation, and eCat for governance—organizations can streamline data migration, enhance security, and ensure accurate, metadata-driven insights. By modernizing workloads and integrating automation at every step, Datametica helps enterprises transform legacy systems into future-ready data ecosystems that drive innovation, reduce costs, and accelerate decision-making.

[datameticasols]

Say Goodbye to Manual Code Conversion with Datametica’s Raven Tool.

Streamline your coding process with Datametica's Raven tool - no more manual code conversion. Get in touch with us to learn how Raven tools

Simplify your cloud data migration with Datametica’s Raven — the intelligent ETL data migration tool designed for automated workload conversion. Raven automates complex code and script transformations while ensuring security, accuracy, and performance. Together with Datametica’s data warehouse assessment tool suite, it delivers a complete, end-to-end cloud modernization experience.

[datameticasols]

Enterprises seeking agility, scalability, and improved performance are increasingly turning to Hadoop to Databricks Migration to modernize their data ecosystems. Traditional Hadoop environments, while once effective, often struggle with performance, cost, and integration challenges in today’s cloud-first landscape. Migrating to Databricks provides a unified platform for data engineering, analytics, and AI, enabling faster insights and reducing operational overhead.

For organizations running Hadoop clusters, Hadoop Migration to GCP offers a pathway to enhanced flexibility, better disaster recovery options, and the ability to consolidate data silos. Google Cloud’s scalable infrastructure and native tools make it ideal for transforming legacy systems into modern, cloud-based architectures.

By leveraging automated migration tools, businesses can reduce costs, improve performance, and unlock advanced analytics capabilities. The result is streamlined operations, simplified data management, and greater access to machine learning and AI insights. Migrating from Hadoop to Databricks on GCP sets the stage for innovation and long-term data success.

[datameticasols]

Why Retailers Should Choose Google Cloud Platform | Datametica

Retailers are currently in the midst of a transformation and a race to leverage the cloud technology to gain a competitive edge, while maxim

As retail goes digital, Google Cloud leads the way — named the #1 cloud provider for the retail sector! From AI-powered customer experiences to scalable omnichannel solutions, GCP empowers retailers to innovate and grow.

With Datametica’s expertise in data warehouse migration & modernization, retailers can ensure a seamless, cost-efficient migration to GCP using automated tools like Eagle, Raven, and Pelican. Together, Datametica and Google Cloud help retailers transform faster, smarter, and at lower risk.

[datameticasols]

Google BigQuery Optimization for Cost & Performance | Datametica

Want to optimize the cost & performance for Google BigQuery? Here is the solution & the best practices to optimize the cost & performance.

Datametica outlines effective strategies to optimize Google BigQuery for both cost and performance—especially valuable during large-scale cloud transitions like Teradata to BigQuery migration or Oracle to BigQuery migration. Since BigQuery charges based on the volume of data scanned, it’s important to avoid querying unnecessary data; using selective column retrieval instead of "SELECT *" is a key best practice. Batch data loads are recommended over streaming inserts when real-time ingestion isn’t essential, and using optimized file formats such as Avro, Parquet, or ORC can enhance loading efficiency. Structuring data using partitioned tables and clustering improves query performance by minimizing the amount of scanned data. Additionally, simplifying SQL by avoiding redundant subqueries, unnecessary ORDER BY clauses, and costly JavaScript UDFs contributes to better performance. Datametica also emphasizes the use of BigQuery’s built-in tools—like the query validator and dry run feature—to estimate query costs before execution. These optimization techniques are especially critical during migrations from legacy systems, ensuring a smooth and cost-efficient transition to BigQuery.

[datameticasols]

Data Migration Challenges and solution for successful implementation

Database migration planning isn’t always as straightforward or simple as it may seem. Here are the top challenges to keep in mind as your or

Data migration is a challenging process, often slowed by redundant data, incomplete documentation, and complex workflows.

Datametica addresses these hurdles with intelligent data migration tools that streamline analysis, automate discovery, and reduce manual effort. Our advanced assessment solution, Eagle, uncovers hidden data dependencies and lineage to build accurate migration roadmaps.

By combining powerful cloud migration tools with expert guidance, Datametica ensures seamless transition from legacy systems to modern cloud platforms — minimizing risk, cost, and time.

Modernize your data journey with Datametica’s automation-driven approach to efficient, secure, and scalable cloud migration.

[datameticasols]

Modernizing Data Infrastructure with Databricks Migration on GCP

As organizations look to modernize their data ecosystems, Databricks Migration provides a seamless transition from legacy on-premise systems to cloud-native solutions. A recent client leveraged this migration to move their existing Data Warehouse from on-premise Oracle and Cloudera Hadoop to Google Cloud Platform (GCP). The objective was clear: build a Unified Data Warehouse using GCP’s cloud-native technologies that would lower operational costs, mitigate risks, and improve overall performance.

The key challenge was to migrate existing data warehouse functionality while maintaining business continuity. With Hadoop to Databricks Migration, the client was able to shift to a scalable and secure cloud platform. By integrating Databricks with GCP, the client could benefit from streamlined data management, enhanced real-time analytics, and improved machine learning capabilities.

An additional requirement was to enable end-users to run SQL queries on Google BigQuery before the full application migration was complete. This helped ensure that the business could continue to operate smoothly during the transition.

This Hadoop to Databricks Migration strategy not only helped the client reduce operational overheads but also allowed them to harness the full potential of cloud-native technologies, driving both innovation and cost efficiency. By embracing Databricks and GCP, the client successfully modernized their data infrastructure for a scalable, future-proof solution.

[datameticasols]

Data Migration Challenges and solution for successful implementation

Database migration planning isn’t always as straightforward or simple as it may seem. Here are the top challenges to keep in mind as your or

Planning a move to the cloud? Successful cloud data migration requires more than just transferring files — it demands strategy, precision, and expertise to avoid risks like downtime, data loss, or compliance issues. At Datametica, we understand the challenges organizations face, from managing legacy systems to ensuring security and regulatory compliance. That’s why our expert data migration services are designed to simplify complex transitions, reduce costs, and maximize efficiency. With proven frameworks and deep industry experience, Datametica helps enterprises migrate seamlessly while unlocking the true potential of their data in the cloud.

[datameticasols]

What is App Dependency and How Does it Impact Your Migration? | Datametica

Application dependency mapping is essential for correctly modernizing a data platform because it ensures that cloud migrations are done in t

A successful cloud data migration isn’t just about moving data—it’s about understanding how your applications are connected. Overlooking app dependencies can create delays, downtime, and extra costs.

In our latest article, we explore how proper planning around application dependencies is key to ensuring smooth and efficient cloud data migration. Learn how the right data migration services can help you identify risks, streamline processes, and keep your migration on track.

[datameticasols]

Best Cloud Cost Optimization Services | Datametica

Discover our professional cloud cost optimization services to effectively manage and reduce expenses in your cloud environment. Contact us f

Take control of your cloud spending with Datametica’s cloud optimization services! Now part of Onix, we offer advanced cloud cost management tools that help businesses monitor, analyze, and reduce unnecessary expenses. Our expert solutions ensure efficient resource allocation, improved performance, and smarter investments. With Cloud Cost Optimization, you can achieve greater savings while maximizing the value of your cloud infrastructure. Trust Datametica, powered by Onix, to help you optimize costs and drive better business outcomes.

[datameticasols]

Data Lake vs Data Warehouse - Datametica Solutions Pvt. Ltd

Unlock the best of both worlds by understanding when to leverage a data lake versus a data warehouse. Data lakes offer flexible, cost-effective storage for raw, unstructured and structured data—ideal for data science and ML use cases—while data warehouses provide structured, curated data for fast, reliable business intelligence and reporting. At Datametica, we begin with a meticulous data warehouse assessment to pinpoint what data assets fit best for structured analysis. From there, our smooth data warehouse migration process ensures your organized, clean data moves efficiently to support smarter decision-making and deeper insights. Ready to modernize your data stack?

[datameticasols]

Pelican Contributed 90% Cost Savings in Data Validation - Datametica Solutions Pvt. Ltd

Datametica’s automated data validation tool Pelican transformed a high‑cost migration project for a major US retailer. The client originally planned to hire 25 BigQuery engineers to validate data during a warehouse migration to Google Cloud Platform, which would have cost over $3.4 million. Manual checking would only sample part of the database and could not be repeated efficiently.

To address these challenges, Datametica deployed Pelican, a specialized solution that automates validation and reconciliation across petabyte‑scale datasets. Unlike generic data validation tools, Pelican requires no coding, compares data at the cell level, runs validations in parallel during migration, and produces detailed discrepancy reports. As a result, the client saved 90 percent of validation costs and eliminated the need for 25 engineers. Pelican also enabled full dataset checks instead of one‑time sampling, providing 100 percent accuracy and the confidence to decommission the legacy system.