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Pymnts
1d
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Beyond the Buzzword: Why Really Big Data Still Matters
- Big data refers to large and complex datasets that traditional methods can't handle.
- Big data revolutionizes industries such as healthcare, weather forecasting, financial markets, shipping, and logistics.
- Challenges of big data include data quality, privacy and security concerns, and the need for specialized tools and infrastructure.
- Big data's impact is crucial for modern research, business operations, and public services as organizations handle ever-larger datasets.
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Amazon
3d
8
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Amazon Q data integration adds DataFrame support and in-prompt context-aware job creation
- Amazon Q data integration allows natural language author ETL jobs in AWS Glue. It introduces exciting new capabilities that make ETL development more efficient and intuitive such as support for DataFrame-based code generation and in-prompt context-aware development.
- The DataFrame code generation works across different data sources and destinations such as Amazon S3 data lakes, relational databases, and NoSQL databases. It can handle complex data processing requirements such as filters, projections, unions, joins, and aggregations.
- Amazon Q data integration simplifies data engineering tasks by providing users with an LCNC ETL workflow. It comes with various capabilities such as prompts, multi-turn chat capabilities, and in-prompt context awareness to incrementally update the data integration flow.
- Amazon Q data integration is available through the Amazon Q chat experience on the AWS Management Console and the Amazon SageMaker Unified Studio preview. The generative visual ETL in the SageMaker Unified Studio also allows refinement of ETL workflow with new requirements, enabling incremental development.
- Amazon Q data integration is available in the SageMaker Unified Studio notebook experience. Users can add a new cell and enter what they want to achieve, and the recommended code is shown.
- Amazon Q data integration is also available in AWS Glue Studio. Users can ask Amazon Q a question to create a Glue ETL flow, and the code with all configurations in place is generated. They can copy and paste the generated code to the script editor and run the job when ready.
- These new capabilities significantly reduce development time and complexity, making it more intuitive and time-efficient for data practitioners building data applications on AWS.
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Amazon
4d
60
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Jumia builds a next-generation data platform with metadata-driven specification frameworks
- Jumia, a Nigeria-based e-commerce company, has transitioned its Hadoop distribution to an AWS serverless platform to build a next-generation data platform with metadata-driven specification frameworks. The company faced issues in terms of increased cost, lack of scalability of computing, job queuing, embracing modern technologies, complex infrastructure automation, and inability for local development.
- The metadata frameworks offer a consistent and efficient approach to data orchestration, migration, ingestion, maintenance and processing. They provide reusability and scalability and streamline the development workflow and minimize the risk of errors. In addition, metadata-driven frameworks adhere to data protection and enforce encryption across all services.
- The architecture consists of frameworks that focus on creating DAGs, dependencies, validations and notifications. Amazon Managed Workflow for Apache Airflow (Amazon MWAA) is used in data orchestration, enabling dynamically created DAGs, natively integrating with non-AWS services, creating dependencies of past executions and generating accessible metadata.
- Another framework involves migrating data from HDFS to Amazon S3 with Apache Iceberg storage format. A metadata-driven framework built in PySpark receives a configuration file and runs migration tasks in an Amazon EMR Serverless job.
- A metadata-driven framework for micro-batch and batch mode was implemented in the data ingestion phase. In the batch mode, the framework is written in PySpark, which extracts data from different data sources (such as Oracle or PostgreSQL). In the micro-batch mode, Spark Structured Streaming ingests data from a Kafka cluster, which has the capability of running native streams in streaming.
- In the data processing phase, Iceberg is used as a delta lake file system. Spark Structured Streaming ingests data from Amazon S3. The maintenance phase involves building a framework that is capable of performing various maintenance tasks on tables within the data lake, including expiring snapshots and removing old metadata files.
- The rearchitected data platform resulted in a 50% reduction in data lake costs, and spawned faster insights and reduced turnaround time to production. It standardized workflows and ways of working across data teams, and created a more reliable source of truth for data assets. The AWS serverless platform also afforded improved scalability, flexibility, integration and cost efficiency.
- Jumia's transformation was led by Hélder Russa, Head of Data Engineering at Jumia Group. Ramón Díez is a Senior Customer Delivery Architect at AWS, while Paula Marenco is a Data Architect at AWS. Pedro Gonçalves is a Principal Data Engineer at Jumia Group.
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TechBullion
5d
44
Image Credit: TechBullion
Shaping the Future of BigData and AI Innovation: The Pioneering Journey of Venkata Suman Doma.
- Venkata Suman Doma exemplifies adaptability, forward-thinking leadership and expertise in the field of enterprise architecture.
- Mr. Doma is an expert in multiple technology stacks, including .NET, SharePoint, Azure and Cognitive Services.
- His specialization in big data technologies, particularly Apache Spark and PySpark, has enabled organizations to transform raw data into actionable insights.
- He has also worked on architecting cloud solutions, helping organizations navigate complexities of digital transformation and creating cohesive technology ecosystems.
- Mr. Doma has been working on automating processes, improving data accuracy, and providing deeper insights into applications using generative AI.
- His role as a liaison between clients, business leadership, infrastructure and development teams ensure that technical solutions meet current needs and anticipate future requirements.
- His extensive certification portfolio reflects his commitment to staying ahead of industry trends.
- As Gartner predicts a cloud-first principle for over 85% of organizations by 2025, it is expected that enterprise architects like Mr. Doma will be proficient in cloud technologies and adept at managing multi-cloud and hybrid environments.
- With the integration of AI, McKinsey & Company estimates that it could generate up to $2.6tn, creating substantial value across various sectors.
- As the enterprise architecture market is projected to grow at a CAGR of 4.8% from USD 1,442.82mn in 2024 to USD 2,308.96mn by 2034, professionals like Mr. Doma will play a crucial role in guiding organizations through technological transformations, ensuring alignment with business goals, and driving innovation.
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TechBullion
5d
203
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Why Every Company Needs a Robust Financial Analytics Strategy to Stay Ahead
- Financial analytics is the secret weapon that can transform your company’s approach to data-driven decision-making.
- A robust financial analytics strategy encompasses systematic processes and tools.
- Advanced analytics techniques are combined with clear business objectives.
- Real-time reporting fosters agility in navigating complex financial landscapes.
- Collaboration between departments, particularly Finance, Marketing, and Operations, is crucial.
- Financial analytics transforms raw data into actionable insights, helping companies understand their financial health.
- It enhances decision-making processes and promotes proactive risk management.
- Companies can pinpoint inefficiencies and eliminate unnecessary costs to improve margins.
- Implementing a strong financial analytics strategy offers numerous advantages for companies striving to gain insights from their data.
- Data security poses a significant challenge, making robust cybersecurity measures essential.
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Amazon
5d
383
Image Credit: Amazon
HEMA accelerates their data governance journey with Amazon DataZone
- HEMA, a Dutch retail brand, has adopted Amazon DataZone to build their data mesh and enable streamlined data access across multiple business areas.
- By using Amazon DataZone, HEMA centralized all data assets across disparate data stacks into a single catalog.
- Amazon DataZone helped HEMA to integrate different systems, such as Databricks and native AWS services.
- HEMA’s adoption strategy was designed on three core principles: Launch it, Prove value, and Be there.
- The adoption plan included allowing the domains to pick up the implementation at their preferred pace before moving onto the next one.
- HEMA found Amazon DataZone easy-to-use that enabled teams to seamlessly search, discover, share, and subscribe to data assets produced within the business.
- HEMA saw massive benefits in terms of significantly reducing the data sharing average turnaround time, enabling teams to develop new use cases for the business, and an energized data organization.
- Amazon DataZone is set to become the central solution for data sharing and data cataloging across the enterprise.
- HEMA is determined to democratize its role by building an efficient data organization that relies on the most advanced data governance solution on the market.
- HEMA launched a series of new features that will continue to improve the data operations, such as data quality scores, data lineage, and fine-grained access control.
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Amazon
5d
228
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Accelerate queries on Apache Iceberg tables through AWS Glue auto compaction
- AWS Glue Data Catalog now supports improved automatic compaction of Iceberg tables for streaming data. Using Apache Iceberg helps to manage transactional support and handling the inflow of small files generated by real-time data streams. The open table formats also support built-in transactional capabilities and mechanisms for compaction.
- AWS Glue’s automatic compaction is helpful in making Iceberg tables in optimal condition by monitoring table partitions and starting the compaction process when specific thresholds for the number of files and file sizes are met. Compaction makes sure that updates to the data result in new files being created, which are then compacted to improve query performance.
- Mentioned features enable businesses to handle large data sets efficiently, enhancing performance, saving costs, and providing faster data processing, shorter query times, and efficient resource utilization. AWS Glue Iceberg with auto compaction proves to be a robust solution for managing high-throughput IoT data streams.
- Data lakes were originally designed to store large volumes of raw, unstructured or semi-structured data at a low cost, serving big data and analytics use cases, but now, data lakes have become essential for various data-driven processes beyond reporting and analytics.
- The organizations have traditionally addressed challenges posed by data lakes through complex extract, transform, and load (ETL) processes, which often led to data duplication and increased complexity in data pipelines. However, to cope with the proliferation of small files, organizations had to develop custom mechanisms to merge these files, leading to the creation of bespoke solutions that were challenging to scale and manage.
- To simplify these challenges, organizations have adopted open table formats (OTFs) like Apache Iceberg, which provide built-in transactional capabilities and mechanisms for compaction. OTFs also address key limitations in traditional data lakes by providing features like ACID transactions, which maintain data consistency across concurrent operations.
- AWS Glue Data Catalog now supports improved automatic compaction of Iceberg tables for streaming data. Using Apache Iceberg helps to manage transactional support and handling the inflow of small files generated by real-time data streams. The open table formats also support built-in transactional capabilities and mechanisms for compaction.
- Automatic compaction in the AWS Glue Data Catalog makes sure that Iceberg tables are always in optimal condition. It constantly monitors table partitions and starts the compaction process when specific thresholds for the number of files and file sizes are met.
- AWS Glue’s automatic compaction is beneficial in making Iceberg tables in optimal condition by monitoring table partitions and starting the compaction process when specific thresholds for the number of files and file sizes are met. Compaction ensures that data updates result in the creation of new files, which are then compacted to improve query performance.
- Mentioned features enable businesses to handle large data sets efficiently, enhancing performance, saving costs, and providing faster data processing, shorter query times, and efficient resource utilization.
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Amazon
5d
363
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Implement a custom subscription workflow for unmanaged Amazon S3 assets published with Amazon DataZone
- Amazon DataZone helps organizations catalog, discover, share, and govern data stored across AWS, on-premises systems, and third-party sources.
- Customers can extend the streamlined data discovery and subscription workflows in Amazon DataZone to unstructured data, according to a recent AWS blog post.
- The post provides a step-by-step tutorial on how to implement a custom subscription workflow using Amazon DataZone, Amazon EventBridge, and AWS Lambda to automate fulfillment for unmanaged data assets such as unstructured data stored in Amazon S3, enhancing governance and simplifying access to unstructured data assets across the organization.
- The solution includes creating a custom subscription workflow that uses the event-driven architecture of Amazon DataZone to handle relevant EventBridge events that will create, cancel, or revoke bucket policies for subscribed S3 assets using an AWS Lambda function.
- The function will ensure that unmanaged S3 asset policies reflect the requests and access control specified in the custom environment with the subscription target.
- Amazon DataZone publishes EventBridge event details about activities within a user's data portal such as subscription requests, comments, and system events based on key activities such as subscription requests, updates, comments, and system events.
- Users search for assets in the custom environment, ask for subscription, and access their data in Amazon SageMaker via their IAM roles.
- The tutorial begins by publishing an unstructured S3 based data asset as S3ObjectCollectionType to Amazon DataZone, creating an AWS service environment, and setting up an IAM role attached to a SageMaker notebook instance.
- After implementing a custom workflow and approving subscriptions, the environment will have access to the unstructured S3 asset.
- Organizations using this custom workflow can now extend the streamlined data discovery and subscription workflows of Amazon DataZone to their unstructured S3 data while maintaining governance and data access control to enhance discovery and access to unstructured data assets across the enterprise.
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Siliconangle
5d
151
Image Credit: Siliconangle
Overture makes its open-source transportation dataset generally available
- The Overture Maps Foundation has made its transportation dataset generally available.
- The dataset includes information about more than 53 million miles of roads worldwide.
- Companies can use this dataset to power ride-sharing apps, logistics software, navigation tools, and more.
- The dataset combines aerial road imagery with information about highway signs, traffic rules, rail and ferry routes.
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Amazon
6d
358
Image Credit: Amazon
Enhancing Search Relevancy with Cohere Rerank 3.5 and Amazon OpenSearch Service
- Traditional keyword-based search methods often fall short in delivering truly relevant results, hence organizations are integrating large language models with Amazon OpenSearch Service.
- Cohere Rerank 3.5 improves search results for Best Matching 25 (BM25), a keyword-based algorithm that performs lexical search.
- Implementation of a reranking pipeline enhances user experience, drives better search outcomes, and improves engagement.
- Amazon OpenSearch Service is a versatile solution for implementing sophisticated search functionality, including the search mechanisms used to power generative AI applications.
- Lexical search relies on exact keyword matching whereas bi-encoders and cross-encoders recognize the context or intent behind the query using semantic search.
- Cohere Rerank 3.5 focuses on enhancing search relevance by reordering initial search results based on deeper semantic understanding of the user query.
- Combining reranking with BM25 reduces the top-20-chunk retrieval failure rate by 67%, delivering a more effective search experience for users.
- Cohere Rerank 3.5, when integrated with OpenSearch, significantly enhances existing project management workflows by increasing accuracy of search results.
- The ease of integration through a single API call with OpenSearch enables quick implementation, offering a competitive edge in user experience without disruption.
- Integrating Cohere Rerank 3.5 with OpenSearch Service is a powerful way to enhance your search functionality and deliver a more relevant search experience.
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TechBullion
2d
197
Image Credit: TechBullion
Transforming Businesses Through Data, Talent, and Innovation: A Conversation with Ragchaabazar Bud, CEO of Finertech
- Ragchaabazar Bud is the CEO of Finertech, a company that is reshaping how businesses harness data, manage talent, and innovate.
- He founded Finertech with a vision to bridge the gap between businesses and their data utilization and talent strategy.
- Finertech provides scalable IT solutions that empower small businesses and startups with enterprise-level capabilities.
- Ragchaabazar emphasizes the balance between innovation and practicality in leadership, the importance of fostering an inclusive team culture, and his dedication to empowering small businesses with enterprise-level capabilities.
- Finertech’s proprietary concept of “Data Alchemy,” which turns raw data into actionable insights, and how his company integrates cutting-edge technology, including AI-powered neural networks.
- We implement tools like AI-powered analytics and predictive modeling to extract actionable insights, helping companies focus on growth and efficiency.
- Ragchaabazar predicts that AI and real-time technology will continue to redefine the business landscape and shares how Finertech is preparing to stay ahead of these changes.
- He predicts that AI will continue to evolve with high speed and get more accurate and in real-time.
- Ragchaabazar's long-term goal for Finertech is to become a trusted global partner for businesses seeking growth through data-driven innovation, seamless product management, and strategic talent solutions.
- He hopes to leave a legacy as a leader who inspired organizations to leverage data, technology, and people to achieve transformative success.
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Startupnation
4d
307
Image Credit: Startupnation
Why Effective Digital Identity Management is Critical for Brand Growth
- Digital identity management has become a major element of online living as people conduct their everyday work online and expect companies to invest in the protection of their personal information.
- Proper digital identity management improves ROIs, increases efficiencies, and differentiates a company from competitors, all while ensuring data security.
- Digital identity management is the set of technologies and processes that allow for the secure sharing of user information and personal data online with companies and organizations.
- With the increasing use of mobile and digital devices, digital identity management has become a crucial issue for every organization operating online.
- Authentication involves verifying a user’s identity to access certain online pages or materials, possibly through usernames and passwords, or more advanced biometric authentications.
- Digital identity management benefits businesses by providing a superior customer experience, improving brand reputation, increasing sales, reducing cost, and enhancing employee experience.
- Organizations must implement specialized identity-as-a-Service (IDaaS) to ensure protection of customer data, employee data, and company information.
- Strong digital identity management helps your company achieve high privacy standards and establishes your brand as reputable and trustworthy.
- Digital identity authorities and systems provide a framework of security that can expand as businesses scale and keep track of reporting, improve compliance with governance and auditing, and foster trust with potential customers.
- Digital identity management is essential to protect customer data, meet compliance requirements, and reduce cybersecurity threats.
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TechBullion
4d
203
Image Credit: TechBullion
Padmaja Pulivarthy: Trailblazing Innovator, Driving Excellence and Sustainability in Database Cloud Computing through Automation and Performance Optimization
- Padmaja Pulivarthy is a prominent innovator in database management, and her contributions in integrating AI and ML into database systems have transformed data-driven decision-making. She has also worked on AI-driven NLP in Oracle databases, enhancing users' experience and streamlining the retrieval of data. Pulivarthy's focus on sustainability has resulted in eco-responsible innovation, including energy-efficient algorithms and workload management. She advocates for democratizing access to database optimization tools and has collaborated with tech firms and research organizations. Pulivarthy has an eye on autonomous databases and dual cloud and edge computing roles. Finally, she is an advocate for education and mentorship in technology.
- Padmaja Pulivarthy has over a decade of expertise in database management, particularly in Oracle, SQL Server, and Greenplum databases. She has worked on big data technologies such as Hadoop, Spark, and Kafka. Her research has integrated AI techniques into traditional database systems to enhance data processing, analysis, and decision-making capabilities.
- Pulivarthy has explored the integration of AI-driven Natural Language Processing (NLP) within Oracle databases, allowing users to interact with data using natural language queries, minimizing query complexity.
- The database automation market is poised for significant expansion to grow from USD 2.35 billion in 2024 to USD 6.99 billion by 2029, at a compound annual growth rate (CAGR) of 24.38%. The growth in automation is driven by the necessity to manage redundant database management processes.
- Automation in database management includes AI and machine learning to provide end-to-end automation for provisioning, security, updates, availability, performance, change management, and error prevention.
- In cloud environments, performance optimization is crucial as it directly impacts application performance, user experience, and business outcomes. Pulivarthy foresees a world where cloud and edge computing synergize seamlessly.
- Padmaja Pulivarthy’s trailblazing work in driving excellence and sustainability in database cloud computing through automation and performance optimization exemplifies the transformative potential of integrating AI and machine learning into database management systems. Her future-forward projects include utilizing quantum algorithms in managing massive datasets and exploring how autonomous databases could serve universal adoption.
- Pulivarthy's pioneering efforts have been recognized globally, earning her accolades from organizations in database management and education. Her achievements serve as a testament to her unwavering dedication to excellence, sustainability, and inclusivity in database technologies.
- Padmaja Pulivarthy has advocated democratizing access to powerful and user-friendly tools for diverse organizations, including small and medium enterprises.
- Based on her significant contributions and research work, Padmaja Pulivarthy has been given the opportunity to serve as a peer reviewer for IEEE, IGI Global, and Springer. Pulivarthy has been honored with a fellowship from prestigious organizations such as IETE.
- Pulivarthy's initiatives and innovations have significantly contributed to decreasing the carbon footprint associated with cloud computing and advocated for eco-responsible innovation.
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Cloudera
5d
20
Image Credit: Cloudera
Key Takeaways from AWS re:Invent 2024
- AWS re:Invent, which is one of the biggest technology conferences of the year, was recently held in Las Vegas.
- AI was the focus of many of the sessions, demonstrations, and conversations at the conference, including Cloudera's acceleration of the development and deployment of AI models.
- In an effort to reduce consumption and costs while improving performance for AI workloads, Cloudera is partnering with AWS to help mutual customers deploy sustainable AI solutions.
- Most customers we spoke with at re:Invent are dealing with data stores across multiple clouds and on-premises environments, making distributed data a key consideration.
- Apache Iceberg was everywhere at re:Invent, with big announcements from AWS, Cloudera, and others. Cloudera’s investment in and support for open metadata standards, our true hybrid architecture, and our native Spark offering for Iceberg combine to make them the ideal Iceberg data lakehouse.
- Another important aspect when approaching AI is providing secure and governed access to trusted data, something that Cloudera's Shared Data Experience (SDX) helps to achieve.
- Cloudera partnered with Mission Cloud to co-host IGNITE24, an event featuring Flo Rida, to celebrate the importance of working together to achieve something greater than the sum of its parts.
- Events like these can be a reminder that, while the work of transforming businesses with data is challenging, it’s also an opportunity to connect, collaborate, and celebrate our shared journey.
- In conclusion, the re:Invent conference provided valuable insights into the importance of AI, sustainability, distributed data, trusted data and open table format.
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Precisely
5d
258
Image Credit: Precisely
How Black Friday Foot Traffic Offers a Glimpse into Retail’s Future
- Retailers have been focusing on hobbies, outdoor activities and arts-and-crafts shops which may signal a cultural pivot toward purchases that foster creativity, connection, and transformation.
- Retailers that integrate digital behavior with physical-world insights gain a decisive edge in today’s evolving market.
- Google Trends showed an unexpected rise in interest in hobby and outdoor stores but retailers using visit intelligence and foot traffic analytics are already capitalizing on these trends to redefine retail’s future.
- A spike in Google searches indicates growing interest in outdoor adventures or home improvement activities but visit data provides businesses with the real-world evidence needed to act decisively.
- Outdoor retailers saw a 3x increase in visits compared to their three-month average, while arts-and-crafts stores recorded a 2x rise this Black Friday reflecting this cultural shift.
- Scheels emerged as a standout performer in the outdoor category, achieving a 13.3% increase in store visits during Black Friday compared to its November average.
- Arts-and-crafts stores like Color Me Mine recorded a 35.7% increase in footfall, while Painting with a Twist and JOANN Fabrics gained 31.8% and 20.7%, respectively, owing to more than materials, like creating spaces for self-expression and connection.
- Outdoor gear, crafting supplies, and hobby kits are no longer just products—they’re gateways to personal growth, creativity, and connection and retail is becoming a space for transformation.
- To stay competitive in this evolving environment, retailers must move beyond transactions and offer experiences that resonate with customers.
- Hobby stores have proven that the future lies in creating spaces where customers connect, learn, and grow.
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