Traditionally, organizations had to choose between data warehouses and data lakes, each with its own strengths and limitations. Data warehouses excelled at providing structured and optimized data storage, but often struggled to accommodate the diversity and volume of modern data. On the other hand, data lakes allowed for flexible and scalable storage of raw data, but faced challenges when it came to organizing and querying that data effectively.

The Data Lakehouse, a term popularized by Databricks, aims to bridge this gap by combining the strengths of both data warehouses and data lakes. It offers a unified platform that supports structured and semi-structured data, enabling users to perform complex analytics, machine learning, and AI (Artificial Intelligence) workloads on a single architecture. The Data Lakehouse architecture provides the foundation for a more streamlined and efficient data management process.

The Databricks Advantage

Databricks, a leading unified analytics platform, has emerged as a pivotal player in the realm of Data Lakehouse solutions. The company’s cloud-based platform integrates data engineering, data science, and business analytics, providing organizations with a collaborative environment to drive innovation and insights from their data.

Key Features of the Databricks Data Lakehouse

Unified Analytics: Databricks’ platform offers a unified approach to analytics, enabling data engineers, data scientists, and analysts to work collaboratively on the same dataset. This eliminates data silos and promotes cross-functional insights.

Scalability: With the ability to process large volumes of data in parallel, Databricks Data Lakehouse solution scales effortlessly to accommodate growing data needs, ensuring high performance even as data volumes increase.

Advanced Analytics: The platform supports advanced analytics capabilities, including machine learning and AI, empowering organizations to derive predictive and prescriptive insights from their data.

Data Governance and Security: Databricks places a strong emphasis on data governance and security, providing features that ensure data quality, lineage, and access control, making it a reliable choice for enterprises dealing with sensitive data.

Ecosystem Integration: Databricks seamlessly integrates with a wide array of data sources, storage systems, and analytics tools, allowing organizations to build and deploy end-to-end data pipelines.

With Microsoft announcing general availability of Fabric in late 2023, organizations are lining up to take advantage of the latest analytical potential of the combined platform. For organizations with Teradata EDW, there can be a high degree of risk to completely modernize with Fabric. Bitwise helps organizations that want to quickly take advantage of cloud cost savings but are not ready for a complete modernization by migrating and stabilizing Teradata EDW to Teradata Vantage on Azure as a stopover solution before modernizing with a ‘better together’ Lakehouse/Fabric architecture for an advanced analytics solution in the cloud.

Organizations with legacy ETL (extract, transform, load) tools like Informatica, DataStage, SSIS (SQL Server Integration Services), and Ab Initio that want to take advantage of programmatical data processing frameworks like Azure Databricks utilizing data lakehouse architecture will find that migration can be a risky proposition due to incompatibility and high probability for human error. This is where Bitwise can overcome challenges and eliminate risk with its AI-powered automation tools backed by years of ETL migration experience to convert legacy code to PySpark for execution in Azure Databricks for improved flexibility to meet modern analytics and AI requirements.

The post Navigating the Data Modernization landscape and diving into the Data Lakehouse concept and frameworks appeared first on Bitwise.

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Ab Initio and AWS Glue Fundamentals for Data Integration

To get started, let’s walk through the fundamentals of Ab Initio and AWS Glue for background and  an overview of each tool.

Ab Initio

Ab Initio has over 25 years of experience with advanced distributed computing systems. You can run your operations on the cloud, on-premise, or in any combination. Whether you want to run across containers with Kubernetes, Unix/Linux boxes, Windows, or mainframes, Ab Initio does it all. You develop your code once and deploy it wherever you need it.
Ab Initio application is a general motive data processing platform for organization-class, mission-relevant applications comparable to data warehousing, batch processing, click stream processing, data movement, data transformation, and analytics. It helps the integration of arbitrary data sources and programs and supplies entire metadata management across the enterprise.
Ab Initio solves essentially the most challenging data processing issues for the leading organizations in telecommunications, finance, insurance, healthcare, e-commerce, retail, transport and different industries whether integrating disparate systems, managing big data, or supporting trade-critical movements. Ab Initio solutions are constructed and employed incredibly fast and provide the best performance and scalability  and are designed from the beginning to provide a single, cohesive technology platform for scalable, High-performance data processing, integration, and governance.
Below we have listed out the main features of Ab Initio.

1. Application specification, design and implementation can be done using Ab Initio graphs/psets in the Graphical Development Environment.
2. Business rules specification and implementation can be specified in the BRE (Business Rules Engine).
3. A single engine for all aspects of application execution in the Co>Operating System.
4. Application orchestration can be performed in Conduct>It.
5. Operational  management can be achieved in the Technical Repository of Ab Initio.
6. Metadata capture, analysis and display can be done in Metadata Hub.
7. Federated queries across virtually any data sources can be performed through a high-performance scalable SQL engine, i.e. Query>It.
8. Data management, including very large data storage (hundreds of terabytes to petabytes), data discovery, analysis, quality and masking can be done using the Testing Framework.

AWS Glue

AWS Glue offers a serverless approach to various data-driven problems and computations such as analytics, machine learning, discovering, preparing, and combining data. It facilitates all data integration services to transform data for good use in a cost-efficient manner. It’s a cloud-based ETL service that is powered by a big data engine to provide data-intensive computation.
AWS Glue architecture consists of three major parts.

• Data Catalog: Data Catalog contains the layout for the heterogeneous data which is used as a source/target of ETL jobs. Data Catalog is a central repository that acts as an index to different data sources. It also contains location and runtime matrices.
• Scheduling: AWS Glue Studio comes with a scheduler that facilitates the users to automatically kick off their series of jobs as per their dependencies or on some trigger-based event.
• ETL Engine: AWS Glue is built on top of Apache Spark, which is powered by the big data Spark architecture. The Spark Cluster leverages efficient and data-intensive computing. It automatically generates the PySpark/Scala code for every ETL job built with the help of a Drag and Drop GUI. The GUI also provides the feature for Workflows which orchestrates several ETL jobs in the required order.

Below we have listed out the main features of AWS Glue.

1. Glue Studio GUI offers GUI Based IDE.
2. Job script code editor gives you the flexibility to edit and write custom ETL logic in the ETL jobs generated scripts. The IDE supports syntax and keyword highlighters and auto-completion for local words, python keywords, and code snippets.
3. Crawler is a program that connects to a data store (source or target), progresses through a prioritized list of classifiers to determine the schema for your data and then creates metadata tables in the AWS Glue Data Catalog.
4. Workflows are created to visualize complex ETL activities involving multiple crawlers, jobs, and triggers. A Workflow can be scheduled daily, weekly, monthly, or can be started manually from the AWS Glue console as per the requirement. With the help of Triggers within the workflows, we can create a large chain of independent jobs and crawlers.

The post Modern Data Integration: Ab Initio or AWS Glue? appeared first on Bitwise.

Flux of transformation and cloud adoption

The post-pandemic timeline has been characterized by a surge in technology adoptions, with numerous businesses transitioning to the cloud. Industries across the board are incorporating cloud as a key component in their data-driven business strategies. To capture the evolving market, increase agility through analytics, and reduce infrastructure costs, leaders recognize the need to induct cloud-driven applications into their data models. But this decision requires forethought and extensive planning.

Experience is the name of the game

It has clearly been established that the choice of a data migration partner is the crux of any transference strategy. A migration partner with an immersive experience in cloud deployment is critical for a successful relocation. They can help you traverse the murky waters of cloud migration while avoiding the pitfalls commonly associated with them.

Bitwise can help expedite your data migration from legacy database applications to the cloud with industry-tested migration solutions, stringent data migration processes, and architectures custom-built for your data combined with operational insights available on integrated visualization dashboards.

Shift data assets to cloud with Bitwise

Bitwise Cloud Data Migration Framework is the final puzzle piece in your migration solution puzzle-box which ensures accelerated data transfer without compromising data integrity. It works in synergy with other cloud migration services, providing feature-rich end-to-end support to enhance your transition journey. The amalgamation of these time-tested tools ensures a seamless client journey.

At the beginning of your cloud journey, our experts study the source software that needs to be relocated. After extensive analysis of the technical and business needs of the project, they outline the optimum migration strategy that should be employed for a non-disruptive transference.

Collaborative and structured approach

As with any technology, there are risks associated with the cloud. Although enterprises deploy the cloud to lower the cost of ownership and elevate data accessibility, the data could be vulnerable to attacks if not properly secured. New data stores are often built ground up directly on the cloud, with the risk of temporary cutoff in case of network failure. Transferring megalith data warehouses takes time and continual oversight, with a lot of downtime if not done correctly.

These scenarios can be avoided if the migration is conducted in an open, portable manner.

• Issues that may crop up pre and post-migration need to be handled with precision. Developers need to layer authenticated security access for admin and different user profiles to tunnel data access.
• Engineers examine all fault scenarios to avoid discrepancies and for rapid error resolution.
• All this, while minimizing downtime, improving output productivity, and enabling data-centric scalable architectures that can handle extreme situations like data influx surges.

In our decades-long involvement with this domain, our coders have encountered many challenges and scaled many mountains to successfully deploy the target specifications. Over the years, we have worked with countless clients in various industries. With each successful deployment, we garnered invaluable insights and periodically re-adjusted our strategy and approach to get the best result.

Real world migration case studies

Using our Cloud Data Migration Framework, Bitwise has helped a variety of customers to successfully migrate data from widely-used legacy systems such as Teradata, Netezza and Oracle to cloud-based data warehouses on AWS Redshift, Azure SQL and Azure Synapse, Google BigQuery and Snowflake.

For a US based Media and Publication company, Bitwise helped move the entire enterprise data warehouse (EDW) data including 600 tables, 420 stored procedures, 120 functions and 50 user define types (overall 115,000 lines of code) from Oracle to Azure SQL Data Warehouse. Read the full Azure SQL migration case study.

For a Direct Banking and Payment Services company, Bitwise migrated the on-premise data warehouse residing on Teradata to cloud-based data warehouse on AWS Redshift, which involved migrating tables, schema mapping and conversion of historical and incremental data. Read the full Redshift migration case study.

Accomplished specialists of the cloud ecosystem

Bitwise houses experts in the field of cloud with extensive experience under their belt. We incorporate proven Data Migration services with an extensive range of customized features to accelerate your cloud journey while ensuring data integrity. Our in-house cloud experts are the best in their fields, and the best people for the job. We help you accelerate your cloud journey, ensuring a seamless migration experience. Our expertise enables you to map your data to the cloud, with a scalable architecture and a robust data migration framework to avoid any future hiccups.

Step up your migration journey with strategically customized utilities, cloud-native data migration services, market-tested cloud data migration framework, and secure customized reference architectures to optimize workloads. Contact us to discuss your cloud migration requirement.

The post Evolve your data game: Map your data stores to the cloud appeared first on Bitwise.

The top five business factors that will make moving your data warehouse to the cloud a wise decision in 2023 will be discussed in this blog post.

1. Scalability and Flexibility:

Scalability is one of the main advantages of migrating your data warehouse to the cloud. With the use of a data warehouse, your company may simply scale up or down its IT needs as needed. For long-term success, businesses are experimenting with a variety of data modeling techniques. Cloud computing once again proves its mettle by being able to grow on demand and adapt to changing requirements because there is no one size fits all answer. Data warehouse modernization offers businesses an infrastructure that meets the purpose as and when necessary without integrating or optimizing difficulties thanks to autonomous scaling or de-scaling of servers, storage, and network bandwidth to manage massive volumes with unprecedented efficiency.

Conclusion:

In 2023, moving your data warehouse to the cloud will offer a variety of business benefits that will transform your company’s data capabilities. The cloud offers a complete solution to unlock the full potential of your data assets, from scalability and cost-effectiveness to improved performance, advanced analytics, and strong security. Businesses may maintain their agility, take quicker, data-driven choices, and gain new insights for innovation and expansion by using the cloud. To maximize the benefits and overcome any potential obstacles, make sure the migration is well-planned and executed with a smooth transition process.

Getting Started

While the benefits are numerous, and the technology matures, there can be many pitfalls on the path to migrating your data warehouse to a cloud environment. Understanding which platform and strategy can best help you achieve your business goals is a crucial first step. An experienced solutions provider should be able to help you conduct your cloud strategy and assessment to develop an implementation roadmap.

The post 5 Business Drivers for migrating your data warehouse to Cloud in 2025 appeared first on Bitwise.