#datalakehouse

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[govindhtech]

Utilize Dell Data Lakehouse To Revolutionize Data Management

Introducing the Most Recent Upgrades to the Dell Data Lakehouse. With the help of automatic schema discovery, Apache Spark, and other tools, your team can transition from regular data administration to creativity.

Dell Data Lakehouse

Businesses’ data management plans are becoming more and more important as they investigate the possibilities of generative artificial intelligence (GenAI). Data quality, timeliness, governance, and security were found to be the main obstacles to successfully implementing and expanding AI in a recent MIT Technology Review Insights survey. It’s evident that having the appropriate platform to arrange and use data is just as important as having data itself.

As part of the AI-ready Data Platform and infrastructure capabilities with the Dell AI Factory, to present the most recent improvements to the Dell Data Lakehouse in collaboration with Starburst. These improvements are intended to empower IT administrators and data engineers alike.

Dell Data Lakehouse Sparks Big Data with Apache Spark

An approach to a single platform that can streamline big data processing and speed up insights is Dell Data Lakehouse + Apache Spark.

Earlier this year, it unveiled the Dell Data Lakehouse to assist address these issues. You can now get rid of data silos, unleash performance at scale, and democratize insights with a turnkey data platform that combines Dell’s AI-optimized hardware with a full-stack software suite and is driven by Starburst and its improved Trino-based query engine.

Through the Dell AI Factory strategy, this are working with Starburst to continue pushing the boundaries with cutting-edge solutions to help you succeed with AI. In addition to those advancements, its are expanding the Dell Data Lakehouse by introducing a fully managed, deeply integrated Apache Spark engine that completely reimagines data preparation and analytics.

Spark’s industry-leading data processing capabilities are now fully integrated into the platform, marking a significant improvement. The Dell Data Lakehouse provides unmatched support for a variety of analytics and AI-driven workloads with to Spark and Trino’s collaboration. It brings speed, scale, and innovation together under one roof, allowing you to deploy the appropriate engine for the right workload and manage everything with ease from the same management console.

Best-in-Class Connectivity to Data Sources

In addition to supporting bespoke Trino connections for special and proprietary data sources, its platform now interacts with more than 50 connectors with ease. The Dell Data Lakehouse reduces data transfer by enabling ad-hoc and interactive analysis across dispersed data silos with a single point of entry to various sources. Users may now extend their access into their distributed data silos from databases like Cassandra, MariaDB, and Redis to additional sources like Google Sheets, local files, or even a bespoke application within your environment.

External Engine Access to Metadata

It have always supported Iceberg as part of its commitment to an open ecology. By allowing other engines like Spark and Flink to safely access information in the Dell Data Lakehouse, it are further furthering to commitment. With optional security features like Transport Layer Security (TLS) and Kerberos, this functionality enables better data discovery, processing, and governance.

Improved Support Experience

Administrators may now produce and download a pre-compiled bundle of full-stack system logs with ease with to it improved support capabilities. By offering a thorough evaluation of system condition, this enhances the support experience by empowering Dell support personnel to promptly identify and address problems.

Automated Schema Discovery

The most recent upgrade simplifies schema discovery, enabling you to find and add data schemas automatically with little assistance from a human. This automation lowers the possibility of human mistake in data integration while increasing efficiency. Schema discovery, for instance, finds the newly added files so that users in the Dell Data Lakehouse may query them when a logging process generates a new log file every hour, rolling over from the log file from the previous hour.

Consulting Services

Use it Professional Services to optimize your Dell Data Lakehouse for better AI results and strategic insights. The professionals will assist with catalog metadata, onboarding data sources, implementing your Data Lakehouse, and streamlining operations by optimizing data pipelines.

Start Exploring

The Dell Demo Center to discover the Dell Data Lakehouse with carefully chosen laboratories in a virtual environment. Get in touch with your Dell account executive to schedule a visit to the Customer Solution Centers in Round Rock, Texas, and Cork, Ireland, for a hands-on experience. You may work with professionals here for a technical in-depth and design session.

Looking Forward

It will be integrating with Apache Spark in early 2025. Large volumes of structured, semi-structured, and unstructured data may be processed for AI use cases in a single environment with to this integration. To encourage you to keep investigating how the Dell Data Lakehouse might satisfy your unique requirements and enable you to get the most out of your investment.

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[feathersoft-info]

Delta Lake Consulting Services & Solutions: Enhancing Data Management

In today’s fast-paced digital environment, organizations are dealing with vast amounts of data, making effective data management a critical business need. Delta Lake, an open-source storage layer that brings reliability to data lakes, helps businesses overcome challenges related to data consistency, scalability, and performance. By implementing Delta Lake, companies can ensure efficient data workflows and better decision-making processes. Delta Lake consulting services provide the expertise needed to fully leverage this powerful solution.

What is Delta Lake?

Delta Lake is an open-source project that enhances traditional data lakes by adding a transactional storage layer to ensure reliable data pipelines. It integrates seamlessly with Apache Spark, making it a preferred solution for companies looking to unify their data architecture.

Some of its key features include:

ACID Transactions: Delta Lake brings atomicity, consistency, isolation, and durability (ACID) to data lakes, ensuring that all changes are trackable and error-free.

Scalability: It supports large-scale data processing, allowing companies to handle petabytes of data efficiently.

Time Travel: Delta Lake enables businesses to query historical data, providing the ability to go back and examine data as it existed at different points in time.

Schema Enforcement: This feature ensures that data is stored consistently, reducing issues caused by schema mismatches and unstructured data.

Why Delta Lake is Essential for Modern Businesses

Unified Data Architecture: Delta Lake allows businesses to unify batch and streaming data. This simplifies the management of real-time data, creating a more holistic view of business performance.

Reliable Data Pipelines: The ACID transactional capabilities of Delta Lake ensure that data processes are resilient, consistent, and reliable. This reduces the risks associated with incomplete data or system crashes during critical updates.

Cost Efficiency: By integrating Delta Lake into your data management system, you can avoid the high costs of inconsistent data or expensive infrastructure. Its support for cloud-based and on-premise environments ensures flexibility and reduced operational costs.

Improved Performance: Delta Lake optimizes query performance by automatically organizing data for fast access and efficient storage management. This leads to faster insights and improved decision-making.

The Role of Delta Lake Consulting Services

While Delta Lake offers significant benefits, navigating its complexities requires expertise. This is where Delta Lake consulting services can help. Consultants guide businesses in effectively implementing, optimizing, and scaling Delta Lake for maximum performance.

Here’s how Delta Lake consulting services can benefit your organization:

Customized Delta Lake Solutions: Every business has different data needs. Delta Lake consultants assess your current data infrastructure and customize Delta Lake implementations to fit your unique requirements.

Seamless Integration: Consultants ensure that Delta Lake is seamlessly integrated into your existing technology stack, such as Apache Spark, Azure, or other cloud platforms. This minimizes disruption and enhances operational efficiency.

Data Strategy Optimization: Delta Lake consultants help optimize your data architecture, improving data quality, storage, and processing. This results in more reliable analytics and faster insights.

Advanced Features Implementation: Whether you need real-time analytics, machine learning integration, or time-travel queries, Delta Lake consultants can set up advanced features that help you gain a competitive edge.

Ongoing Support and Training: Engaging with Delta Lake consulting services means you receive not just implementation support but ongoing training for your team. This ensures that your staff can fully utilize Delta Lake and achieve maximum value from your data investments.

Choosing the Right Delta Lake Consulting Partner

Selecting the right Delta Lake consulting partner is crucial to the success of your data initiatives. Look for a provider with a proven track record in Delta Lake implementations, deep knowledge of data lakes, and experience with cloud-based solutions.

Key considerations when choosing a Delta Lake consulting partner:

Expertise in large-scale data processing and Apache Spark.

Proven ability to deliver customized solutions.

Strong industry experience to ensure alignment with business goals.

A focus on long-term support and training to maximize your data’s potential.

Conclusion

Delta Lake is a game-changer for businesses looking to enhance their data management practices. By working with expert consultants, organizations can streamline data workflows, ensure high-performance analytics, and reduce operational inefficiencies.

If you're ready to unlock the full potential of Delta Lake, reach out to Feathersoft Company for tailored Delta Lake consulting services.

[nuvento-sys]

Nuvento helps businesses architect and migrate to the Databricks data lakehouse. We also build Brickbuilder (data and AI) solutions for industry-specific use cases using Databricks capabilities by implementing the lakehouse architecture. Our team of dedicated Databricks experts will further help customers implement and scale data engineering, collaborative data science, full-lifecycle machine learning, and business analytics initiatives. Read more: https://nuvento.com/databricks-partner/

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RECENT APACHE ICEBERG LAKEHOUSE CONTENT

—-Recent Articles—-

- Iceberg hybrid lakehouses: https://bit.ly/3VkCfV8

- How iceberg optimizes queries: https://lnkd.in/eF5mNuF4

- Apache Iceberg Wins: https://lnkd.in/eRXEbHA8

- Nessie Ecosystem: https://lnkd.in/e-SW9_3m

- Deep dive on Dremio Reflections: https://lnkd.in/ej7cHJsj

- Evolution of Iceberg Catalogs: https://lnkd.in/ekrdkqRy

—-Tutorials—-

- Intro to iceberg exercise: https://lnkd.in/eeq7DZiG

- Iceberg with git for data & dbt: https://lnkd.in/eR8mKvkk

#DataEngineering #ApacheIceberg #BigData #DataLakehouse

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AI Surge is the new way of data management Unlike the existing solutions, AI Surge will not only build your data pipeline in minutes, but you won’t have to be technology savvy or write code to get insight from your Data

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[govindhtech]

IBM Data Product Hub: Customers Get Business Intelligence

How IBM Data Product Hub can help you realize the full potential of business intelligence. Users of business intelligence (BI) often have difficulties obtaining the relevant, high-quality data required to support strategic decision making.

Data Product Hub

These experts run across a number of problems while trying to get the information they want, such as:

Problems with data accessibility include the difficulty to find and access certain data because it is stored in isolated systems or requires many permissions, which causes delays and bottlenecks.

Inconsistent data quality: Analyses and reports may be at risk due to the lack of assurance about the dependability, consistency, and correctness of data gathered from diverse sources.

Time-consuming data requests: The reporting process may be hampered by delays of days or even weeks resulting from the dependence on data engineering teams to complete data requests.

Lack of openness on terms of data usage: If data is utilized incorrectly or beyond its intended scope, the lack of clear norms and regulations governing data use may put compliance at risk.

The difficulties posed by these issues may significantly affect an organization’s capacity to make wise choices. Consequences consist of:

Lost productivity: Spent time and money looking for information that isn’t easily accessible.

Inaccurate insights: When uneven data quality undermines the validity of analysis and reporting.

Decision-making that is delayed: Slow data availability causes delays in making strategic decisions.

Risks associated with compliance: Putting companies at risk of penalties and harm to their reputation for not being transparent about the rules of data usage.

The path ahead: Data goods made available via data marketplaces

Organizations may get faster, more dependable access to high-quality data by using the power of data markets and data products, which will eventually lead to quicker and better-informed decision-making. Data markets enable businesses to bundle and distribute data as data products, which helps to address these issues.

Data products are collections of dashboards, repeatable queries, and controlled, regulated datasets. For analytics, artificial intelligence, and other crucial data tasks, they are made to be easily used by corporate leaders, business analysts, data analysts, and other data consumers. These goods may be controlled by pre-established data contracts and are readily discoverable and consistent.

The IBM method

Data producers may build and manage data products using IBM Data Product Hub, a data sharing service. Important characteristics including business domain, access level, distribution methods, suggested use, and data contracts are all considered while curating these items. It makes data sharing and access easier by enabling users of data lakehouses and data warehouses to bundle their data assets as data products.

Controlled data exchange

With a clear knowledge of use rights and restrictions, the data contract functionality helps guarantee that data may be shared with customers in a regulated and transparent manner. Data producers may increase productivity by eliminating the need to continually fulfill comparable data requests by packaging assets as reusable data products.

Quick access to superior quality data

Customers no longer have to wait as long to get high-quality data from various data sources thanks to this method. Because the data products are reusable, data lakehouse storage and processing expenses for well chosen assets are reduced.

Improved accessibility and efficiency

The ability to use data from a data lakehouse is improved by the capacity to bundle assets kept in data lakes as data products on IBM Data Product Hub. Accessible across the company, data goods alleviate problems for technical and commercial producers alike.

Streamlined transmission of data

Data producers may use the flight service delivery technique to supply data products to data consumers via data extract or live access. In addition to placing a subscription request, data consumers may examine business use cases, important features, and data contracts related to a data product.

Data products and BI reports

With the current version of IBM Data Product Hub, a technical data consumer may use data products given via the flight service delivery mechanism, which allows live data access without moving data, to build business intelligence reports. In Microsoft Power BI, the user may establish a “Python script” connection and begin generating BI reports for more BI analysis on data products.

The JSON string that was supplied with them during data product delivery is used by the user to authenticate themselves to the server when the Python script executes in Power BI. This enables Power BI users to have real-time, read-only access to data assets kept in a data lakehouse.

IBM’s new data sharing software provides BI customers with a cutting-edge solution to get over the conventional problems associated with data access. Through the packaging of carefully selected, superior data assets into data products, companies may contribute to the efficient, regulated, and uniform supply of data. As a result, BI users are able to produce reports more quickly and with more assurance about the integrity and control of the data they are utilizing, which eventually leads to improved business results.

Read more on Govindhtech.com

[govindhtech]

IBM Watsonx.governance Removes Gen AI Adoption Obstacles

The IBM Watsonx platform, which consists of Watsonx.ai, Watsonx.data, and Watsonx.governance, removes obstacles to the implementation of generative AI.

Complex data environments, a shortage of AI-skilled workers, and AI governance frameworks that consider all compliance requirements put businesses at risk as they explore generative AI’s potential.

Generative AI requires even more specific abilities, such as managing massive, diverse data sets and navigating ethical concerns due to its unpredictable results.

IBM is well-positioned to assist companies in addressing these issues because of its vast expertise using AI at scale. The IBM Watsonx AI and data platform provides solutions that increase the accessibility and actionability of AI while facilitating data access and delivering built-in governance, thereby addressing skills, data, and compliance challenges. With the combination, businesses may fully utilize AI to accomplish their goals.

Forrester Research’s The Forrester Wave: AI/ML Platforms, Q3, 2024, by Mike Gualtieri and Rowan Curran, published on August 29, 2024, is happy to inform that IBM has been rated as a strong performer.

IBM is said to provide a “one-stop AI platform that can run in any cloud” by the Forrester Report. Three key competencies enable IBM Watsonx to fulfill its goal of becoming a one-stop shop for AI platforms: Using Watsonx.ai, models, including foundation models, may be trained and used. To store, process, and manage AI data, use watsonx.data. To oversee and keep an eye on all AI activity, use watsonx.governance.

Watsonx.ai

Watsonx.ai: a pragmatic method for bridging the AI skills gap

The lack of qualified personnel is a significant obstacle to AI adoption, as indicated by IBM’s 2024 “Global AI Adoption Index,” where 33% of businesses cite this as their top concern. Developing and implementing AI models calls both certain technical expertise as well as the appropriate resources, which many firms find difficult to come by. By combining generative AI with conventional machine learning, IBM Watsonx.ai aims to solve these problems. It consists of runtimes, models, tools, and APIs that make developing and implementing AI systems easier and more scalable.

Let’s say a mid-sized retailer wants to use demand forecasting powered by artificial intelligence. Creating, training, and deploying machine learning (ML) models would often require putting together a team of data scientists, which is an expensive and time-consuming procedure. The reference customers questioned for The Forrester Wave AI/ML Platforms, Q3 2024 report said that even enterprises with low AI knowledge can quickly construct and refine models with watsonx.ai’s “easy-to-use tools for generative AI development and model training .”

For creating, honing, and optimizing both generative and conventional AI/ML models and applications, IBM Watsonx.ai offers a wealth of resources. To train a model for a specific purpose, AI developers can enhance the performance of pre-trained foundation models (FM) by fine-tuning parameters efficiently through the Tuning Studio. Prompt Lab, a UI-based tools environment offered by Watsonx.ai, makes use of prompt engineering strategies and conversational engagements with FMs.

Because of this, it’s simple for AI developers to test many models and learn which one fits the data the best or what needs more fine tuning. The watsonx.ai AutoAI tool, which uses automated machine learning (ML) training to evaluate a data set and apply algorithms, transformations, and parameter settings to produce the best prediction models, is another tool available to model makers.

It is their belief that the acknowledgement from Forrester further confirms IBM’s unique strategy for providing enterprise-grade foundation models, assisting customers in expediting the integration of generative AI into their operational processes while reducing the risks associated with foundation models.

The watsonx.ai AI studio considerably accelerates AI deployment to suit business demands with its collection of pre-trained, open-source, and bespoke foundation models from third parties, in addition to its own flagship Granite series. Watsonx.ai makes AI more approachable and indispensable to business operations by offering these potent tools that help companies close the skills gap in AI and expedite their AI initiatives.

Watsonx.data

Real-world methods for addressing data complexity using Watsonx.data

As per 25% of enterprises, data complexity continues to be a significant hindrance for businesses attempting to utilize artificial intelligence. It can be extremely daunting to deal with the daily amount of data generated, particularly when it is dispersed throughout several systems and formats. These problems are addressed by IBM Watsonx.Data, an open, hybrid, and controlled data store that is suitable for its intended use.

Its open data lakehouse architecture centralizes data preparation and access, enabling tasks related to artificial intelligence and analytics. Consider, for one, a multinational manufacturing corporation whose data is dispersed among several regional offices. Teams would have to put in weeks of work only to prepare this data manually in order to consolidate it for AI purposes.

By providing a uniform platform that makes data from multiple sources more accessible and controllable, Watsonx.data can help to simplify this. To make the process of consuming data easier, the Watsonx platform also has more than 60 data connections. The software automatically displays summary statistics and frequency when viewed data assets. This makes it easier to quickly understand the content of the datasets and frees up a business to concentrate on developing its predictive maintenance models, for example, rather than becoming bogged down in data manipulation.

Additionally, IBM has observed via a number of client engagement projects that organizations can reduce the cost of data processing by utilizing Watsonx.data‘s workload optimization, which increases the affordability of AI initiatives.

In the end, AI solutions are only as good as the underlying data. A comprehensive data flow or pipeline can be created by combining the broad capabilities of the Watsonx platform for data intake, transformation, and annotation. For example, the platform’s pipeline editor makes it possible to orchestrate operations from data intake to model training and deployment in an easy-to-use manner.

As a result, the data scientists who create the data applications and the ModelOps engineers who implement them in real-world settings work together more frequently. Watsonx can assist enterprises in managing their complex data environments and reducing data silos, while also gaining useful insights from their data projects and AI initiatives. Watsonx does this by providing comprehensive data management and preparation capabilities.

Watsonx.Governance

Using Watsonx.Governance to address ethical issues: fostering openness to establish trust

With ethical concerns ranking as a top obstacle for 23% of firms, these issues have become a significant hurdle as AI becomes more integrated into company operations. In industries like finance and healthcare, where AI decisions can have far-reaching effects, fundamental concerns like bias, model drift, and regulatory compliance are particularly important. With its systematic approach to transparent and accountable management of AI models, IBM Watsonx.governance aims to address these issues.

The organization can automate tasks like identifying bias and drift, doing what-if scenario studies, automatically capturing metadata at every step, and using real-time HAP/PII filters by using watsonx.governance to monitor and document its AI model landscape. This supports organizations’ long-term ethical performance.

By incorporating these specifications into legally binding policies, Watsonx.governance also assists companies in staying ahead of regulatory developments, including the upcoming EU AI Act. By doing this, risks are reduced and enterprise trust among stakeholders, including consumers and regulators, is strengthened. Organizations can facilitate the responsible use of AI and explainability across various AI platforms and contexts by offering tools that improve accountability and transparency. These tools may include creating and automating workflows to operationalize best practices AI governance.

Watsonx.governance also assists enterprises in directly addressing ethical issues, guaranteeing that their AI models are trustworthy and compliant at every phase of the AI lifecycle.

IBM’s dedication to preparing businesses for the future through seamless AI integration

IBM’s AI strategy is based on the real-world requirements of business operations. IBM offers a “one-stop AI platform” that helps companies grow their AI activities across hybrid cloud environments, as noted by Forrester in their research. IBM offers the tools necessary to successfully integrate AI into key business processes. Watsonx.ai empowers developers and model builders to support the creation of AI applications, while Watsonx.data streamlines data management. Watsonx.governance manages, monitors, and governs AI applications and models.

As generative AI develops, businesses require partners that are fully versed in both the technology and the difficulties it poses. IBM has demonstrated its commitment to open-source principles through its design, as evidenced by the release of a family of essential Granite Code, Time Series, Language, and GeoSpatial models under a permissive Apache 2.0 license on Hugging Face. This move allowed for widespread and unrestricted commercial use.

Watsonx is helping IBM create a future where AI improves routine business operations and results, not just helping people accept AI.

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