Latest news with #ApacheSpark
Cision Canada
2 days ago
- Business
- Cision Canada
Databricks Donates Declarative Pipelines to Apache Spark™ Open Source Project
SAN FRANCISCO, June 11, 2025 /CNW/ -- Data + AI Summit -- Databricks, the Data and AI company, today announced it is open-sourcing the company's core declarative ETL framework as Apache Spark™ Declarative Pipelines. This initiative comes on the heels of Apache Spark reaching two billion downloads and the recent launch of Apache Spark 4.0. These releases build on Databricks' long-standing commitment to open ecosystems, ensuring users have the flexibility and control they need without vendor lock-in. Spark Declarative Pipelines tackles one of the biggest challenges in data engineering, making it easy to build and operate reliable, scalable data pipelines end-to-end. Spark Declarative Pipelines provides an easier way to define and execute data pipelines for both batch and streaming ETL workloads across any Apache Spark-supported data source, including cloud storage, message buses, change data feeds and external systems. This battle-tested declarative framework for building data pipelines helps engineers address common pain points like complex pipeline authoring, manual operations overhead and siloed batch/streaming. Spark Declarative Pipelines is based on Databricks' core declarative ETL framework, which is used by thousands of customers. With the proven ability to handle complex data engineering workloads and low-latency streaming, Spark Declarative Pipelines lays the foundation for the next generation of data processing and governance. With Spark Declarative Pipelines, more community members can begin to cut engineering time and costs and reliably support new AI agent systems and other workloads in production. "Our commitment to open source is unwavering. With origins in academia and the open source community, Databricks was founded in 2013 by the original creators of the lakehouse architecture and open source projects including Apache Spark, Delta Lake, MLflow and Unity Catalog," said Matei Zaharia, Co-founder and CTO of Databricks. "We worked closely with the community to help remove friction around data formats that kept information siloed. Spark Declarative Pipelines now gives enterprises an open way to build high-quality pipelines." Key benefits of Spark Declarative Pipelines include: Simplifying pipeline authoring: Data engineers and analysts can quickly declare robust pipelines with minimal coding, focusing on delivering business-critical insights. Improved operability by design: Spark Declarative Pipelines help catch issues earlier in development through clear pipeline definitions that are validated in full prior to execution, reducing the risk of failures downstream and making pipelines easier to troubleshoot and maintain. Unified batch and streaming: Data teams can flexibly meet both real-time and periodic processing needs through a single API for defining and managing batch and streaming data pipelines, simplifying development and maintenance. "Declarative pipelines hide the complexity of modern data engineering under a simple, intuitive programming model. As an engineering manager, I love the fact that my engineers can focus on what matters most to the business. It's exciting to see this level of innovation now being open-sourced, making it accessible to even more teams." — Jian (Miracle) Zhou, Senior Engineering Manager, Navy Federal Credit Union "At 84.51˚ we're always looking for ways to make our data pipelines easier to build and maintain, especially as we move toward more open and flexible tools. The declarative approach has been a big help in reducing the amount of code we have to manage, and it's made it easier to support both batch and streaming without stitching together separate systems. Open-sourcing this framework as Spark Declarative Pipelines is a great step for the Spark community." — Brad Turnbaugh, Sr. Data Engineer, 84.51° About Databricks Databricks is the Data and AI company. More than 15,000 organizations worldwide — including Block, Comcast, Condé Nast, Rivian, Shell and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake, MLflow, and Unity Catalog. To learn more, follow Databricks on X, LinkedIn and Facebook.
Time Business News
01-05-2025
- Business
- Time Business News
Top Challenges in Data Science Today and How to Overcome Them
You must have heard data science is continuously making headlines in the newspapers and magazines. It is impacting every field of our lives. From driving insights to innovations, it is amazing to see how data science is truly transforming everything around us. Data science domain is changing rapidly and is complex to deal with. Thus, it provides its own challenges and issues to deal with. Solving these problems requires skills which can be gained through free online data science courses. Several free or paid data science certification courses have made it easier to upskill. However, some practical challenges still remain. By reading this article, you will explore three top data science challenges and their solutions. Data science is transforming industries, but it also has some challenges. Professionals must overcome these challenges to use data science to its maximum potential. Now, in this part of the article, I will mention three main challenges in data science. I'm also going to tell you about their solutions. Do you know that in 2020, we generated 64.2 ZB of data, more than the number of detectable stars in the cosmos? Not only this, but experts predict that these figures will continue to rise. By the end of 2024, we are expected to generate 14 ZB data. Hence, we generate a huge amount of data daily. Organisations often find it challenging to manage and efficiently process such big datasets. Many conventional tools are insufficient to deal with such datasets, which are larger than terabytes or petabytes. This results in bottlenecks and inefficiencies. Another challenge is processing and getting insights from such huge datasets on time. This process requires scalable infrastructure and mechanisms. To solve this problem, companies should use distributed computing frameworks like Apache Spark and Hadoop. These platforms efficiently handle big data by breaking huge datasets into smaller chunks. Then, these are processed in parallel across many nodes. Apache Spark has its in-memory processing capabilities. This allows it to deliver faster results. On the other hand, Hadoop has robust data storage because of its HDFS (Hadoop Distributed File System). Therefore, companies can better scale their data processing and analysis on time by using these frameworks. The second most common data science challenge is poor data quality and integrity. This issue can derail or delay even the most advanced analytics projects. Why so? Because missing values, duplicate entries, and inconsistent formats lead to false predictions. This, in turn, generates wrong insights, which can hamper the project. If companies fail to ensure data quality and integrity, then their decisions will be based on unreliable information. It will further impact their reputation and trust among the public. It can also lead a company to face legal or regulatory challenges. The solution to the second problem is ensuring robust data cleaning and validation pipelines. These two things are foremost important to maintain data quality and integrity. Companies use tools like Pandas to handle these issues. It is a Python library that allows efficient manipulation and cleaning of structured data. Another thing is using automated ETL (Extract, Transform, Load) processes to streamline these workflows. ETL tools automate repetitive tasks such as removing duplicates and standardising formats. Moreover, real-time data validation systems can be used to prevent errors even before they occur. These kinds of systems flag errors at the source. Thus saving time and resources. The third challenge in data science is bridging the talent and skills gap. The demand for data science is growing, but there's also a shortage of skilled professionals. Many organisations find it difficult to find candidates who have both technical and domain-specific expertise. This mismatch in skills is evident during placement in colleges and universities. Beginner-level professionals find it challenging to crack the interview process. The education industry has not been able to cope with the demands of the dynamic data science industry. This gap can slow innovation and limit the impact of data science in the long run. Cross-functional collaboration among different teams and departments should be promoted to address the talent gap challenge. Diverse teams should be created wherein domain experts will work with data scientists. This can significantly reduce the gap between technical and industry-specific knowledge. Additionally, AutoML (Automated Machine Learning) tools like and Google AutoML should be adopted in the industry. It will allow non-technical stakeholders to contribute to data science projects. That, too, without using extensive programming skills. Moreover, businesses should also invest in upskilling programs for their existing employees. Companies should encourage employees to enrol in online data science courses if some employees find it challenging to join offline courses. These courses will allow them to learn crucial skills in machine learning, data visualisation, and statistical modelling. Trusted and reputed platforms like offer online data science courses which allow you to learn at your own pace. If you are worried about the cost or huge tuition fees, the institution also provides free data science courses to learn without worrying about financial constraints. Data science is a booming industry and affects every aspect of our lives. However, there are several challenges which come across while implementing it. It is essential to solve these issues to unlock the true potential of data science. Professionals should embrace diverse tools and techniques to solve such challenges. Moreover, professionals should enrol in online data science courses to upskill themselves for flexible learning. For those worried about cost issues, several platforms like offer free online data science courses for beginners and professionals. TIME BUSINESS NEWS
Time Business News
24-04-2025
- Business
- Time Business News
What Skills Are Needed For Big Data Careers In 2025?
In today's data-driven world, Big Data is no longer just a buzzword—it's a fundamental part of how businesses operate and innovate. With the global volume of data expected to reach 175 zettabytes by 2025 (IDC, 2021), organizations increasingly rely on Big Data professionals to make sense of it all. But what does it take to pursue a successful career in Big Data in 2025? The Big Data industry is rapidly evolving, powered by advancements in artificial intelligence, machine learning, edge computing, and cloud technology. This shift is not just about tools, but also about mindset, agility, and the ability to adapt to new challenges. This article will explore the key skills you'll need to thrive in a Big Data career in 2025. According to Glassdoor, the average salary for a Big Data Engineer is ₹12,00,000 in India (2025). [1] At the core of every Big Data job is the ability to work with data programmatically. Welcome to Infycle Technologies, your gateway to mastering Big Data! Our comprehensive Big Data Training in Chennai is meticulously designed to align with industry standards, offering a blend of theoretical knowledge and practical, hands-on training. This means being comfortable with coding, not just at a basic level, but with a deep understanding of how to efficiently manipulate and analyze large volumes of data. Python stands out as the leading language in this field thanks to its readability and the wide range of libraries available for data processing and machine learning. Libraries such as Pandas, NumPy, and PySpark are commonly used in industry settings. Additionally, knowledge of Java or Scala remains valuable, particularly when working with Big Data frameworks like Apache Hadoop or Apache Spark. SQL also remains crucial—understanding how to query and manipulate relational databases is a fundamental requirement. While you don't need to be a software engineer, the ability to write and understand code will give you a strong edge. Understanding how to handle massive datasets requires familiarity with specific frameworks designed for distributed computing: Apache Hadoop remains a foundational concept, though its usage has declined in favor of faster tools. Apache Spark is the leading framework due to its speed and support for both batch and real-time data processing. Familiarity with Apache Kafka is important for managing real-time data streams. Newer tools like Apache Flink are valuable for advanced, real-time analytics applications. These tools collectively form the core of modern data infrastructure and are essential for most Big Data roles. Proficiency in cloud-native data tools such as Amazon EMR, Google BigQuery, and Azure Synapse Analytics is becoming standard in enterprise settings. Knowing how to deploy and optimize data workflows in the cloud is now a critical, in-demand skill. One of the most sought-after roles in the Big Data world is the data engineer. This position involves developing and managing data pipelines to ensure seamless data transfer from source to storage to analytics. You need a good grasp of ETL (Extract, Transform, Load) processes and data pipeline orchestration tools such as Apache Airflow or Prefect to excel here. You'll also be expected to understand data warehousing concepts and be familiar with technologies like Snowflake, Amazon Redshift, and Delta Lake. A solid understanding of data modeling, architecture, and storage formats (like Parquet, Avro, and ORC) can make a significant difference when designing efficient systems. It's one thing to collect and process data—it's another to interpret it meaningfully. Data analysts and data scientists must be able to extract actionable insights from complex datasets, which requires solid knowledge of statistical analysis, hypothesis testing, and data exploration. Tools like Tableau, Power BI, and Looker are commonly used to present insights in visually engaging ways. Python's Matplotlib and Seaborn libraries offer great flexibility for those who prefer open-source tools. The key is not just to visualize data but to tell a story that supports decision-making. Answering the 'why' behind the data is as important as the 'what.' As artificial intelligence becomes increasingly integrated with Big Data applications, knowledge of machine learning techniques has become essential. Many organizations now use predictive analytics and automated decision-making systems to gain a competitive edge. This has led to a growing market for professionals who understand both supervised and unsupervised learning techniques, as well as deep learning. Frameworks like TensorFlow, PyTorch, and Scikit-learn are popular in this space. If you're building models at scale, tools like Spark MLlib can help integrate machine learning into Big Data workflows. Moreover, familiarity with MLOps practices—such as model versioning, deployment, and monitoring—will become a key differentiator in 2025. The migration of Big Data systems to the cloud is no longer optional—it's the new normal. Professionals are expected to know how to work with cloud platforms like AWS, Google Cloud, and Azure. Beyond storage, cloud platforms offer powerful services for data analytics, machine learning, and automation. You should be comfortable setting up data pipelines using services like AWS Glue or Azure Data Factory and know how to manage permissions, data security, and cost-effective architecture. Earning certifications from cloud providers can validate your skills and open new doors professionally. Working with both structured and unstructured data is important in the Big Data world. This means understanding when to use traditional relational databases like PostgreSQL or MySQL and when to switch to NoSQL databases like MongoDB or Cassandra. You may also encounter graph databases like Neo4j, especially in applications involving relationships between data points (like social networks or fraud detection). The ability to choose and manage the right type of database for a given task is a crucial part of any data professional's toolkit. As data privacy regulations continue to tighten globally, Big Data professionals must pay close attention to how data is stored, protected, and shared. Understanding data governance isn't just for compliance teams—it's now part of every data role. This includes maintaining data integrity, implementing role-based access controls, ensuring encryption at rest and in transit, and monitoring for unauthorized access. A strong foundation in data ethics and legal compliance (e.g., GDPR, CCPA) will also be vital as data breaches become more costly and public. A successful Big Data career isn't just about crunching numbers—it's about solving real business problems. That's why a strong understanding of your business domain can set you apart from others with similar technical skills. Whether it's finance, healthcare, retail, or manufacturing, domain knowledge helps contextualize your data and makes your insights more impactful. In 2025, professionals who can bridge the gap between data and decision-making by understanding both the technical and business aspects of an organization will be especially valuable. While technical skills are essential, soft skills often determine how effectively you can function within a team and communicate your findings. Unlock your possibility and build a rewarding career in software development with Infycle Technologies, the leading IT Training Institute in Chennai . Working collaboratively, especially in diverse and cross-functional teams, is increasingly important in hybrid work environments. Strong communication skills help you explain complex technical ideas to non-technical stakeholders. Critical thinking and problem-solving enable you to approach challenges creatively. And perhaps most importantly, a willingness to continuously learn and adapt ensures you can stay relevant as technology evolves. In a field that changes as quickly as Big Data, continuous learning isn't just helpful—it's necessary. Earning certifications from reputable platforms or cloud providers helps validate your skills and shows commitment to growth. Courses and certifications from AWS, Microsoft Azure, Google Cloud, Cloudera, and Databricks can greatly enhance your credibility. Even beyond certifications, participating in online courses, webinars, hackathons, and open-source projects will keep your knowledge sharp and your skills up to date. The Big Data landscape in 2025 will be more advanced, complex, and integrated with every aspect of digital business. To succeed, professionals need a holistic skill set that combines technical expertise with analytical thinking, business understanding, and a proactive mindset toward learning. Whether you're just starting or looking to transition into a Big Data career, now is the time to build these essential skills. Focus on strengthening your programming foundation, getting hands-on with modern data tools, and understanding how your work connects to real-world impact. Reference Link: TIME BUSINESS NEWS
Forbes
25-03-2025
- Business
- Forbes
Big Data Engineering: The Fuel Powering AI In The Digital Age
Shinoy Vengaramkode Bhaskaran, Senior Big Data Engineering Manager, Zoom Communications Inc. As a data engineering leader with over 15 years of experience designing and deploying large-scale data architectures across industries, I've seen countless AI projects stumble, not because of flawed algorithms but because the underlying data pipelines were weak or chaotic. These real-world struggles inspired me to write the book, Hands-On Big Data Engineering: From Architecture to Deployment, to guide companies on building scalable, AI-ready data systems. This article explores key insights from Hands-On Big Data Engineering, discussing why data engineering is critical in the AI-driven era, how enterprises can harness it for innovation and what the future holds for AI-driven data architectures. AI is reshaping industries from finance and healthcare to e-commerce and logistics. However, the real driving force behind AI's success is data. A 2024 study by MIT Technology Review Insights and Snowflake found that 78% of companies feel at least somewhat unequipped to deploy generative AI at scale, with weak data strategies being the prevailing issue. A 2024 Rand report also found that inadequate data infrastructure is a major factor in AI projects failing. In today's digital economy, data isn't just fuel—it's the foundation. Big Data and AI are deeply interconnected: Data fuels AI models, and AI enhances data processing. AI's effectiveness depends on three key aspects of Big Data: AI models thrive when trained on vast datasets. Platforms like Netflix process petabytes of user data weekly to improve recommendations. Similarly, the automotive industry relies on terabytes of sensor data to train autonomous vehicles. Handling such scale requires distributed storage systems like HDFS and cloud object stores, paired with scalable frameworks like Apache Spark. AI relies on diverse data types: structured transactional logs, semi-structured JSON and unstructured images, videos and social posts. Predictive healthcare models combine structured electronic health records (EHR) data with unstructured doctor notes and medical images. Data engineers build pipelines to unify these sources, often using Apache NiFi and schema evolution techniques. AI models in fraud detection and predictive maintenance rely on real-time data. Financial institutions process transactions within milliseconds to detect fraud before payments are completed. This speed depends on streaming tools like Apache Kafka, paired with Apache Flink for fast processing—the backbone of modern real-time data architectures. Simply having data isn't enough. AI's performance depends on data quality, structure and accessibility—all enabled by strong data engineering. While data science and AI get the spotlight, data engineering is the unsung hero behind successful AI systems. Let's explore why data engineering is the backbone of AI: Enterprises collect data from IoT devices, social platforms and legacy systems. The challenge is integrating them into high-quality, unified datasets. For example, healthcare providers often struggle to merge legacy EHR data with wearable device data. This is why data engineers built ETL pipelines to clean, normalize and unify this data, addressing: • Data Inconsistency: Incomplete or inaccurate records bias models. • Data Integration: Structured and unstructured data must coexist in AI-ready formats. • Scalability: Pipelines must adapt as new data sources emerge. Relational databases were never designed for AI-scale workloads. The shift to cloud-native systems, Hadoop and Spark reflects the need for massive parallel processing. One retailer I worked with reduced recommendation engine training time by 90% by switching from a relational database to Apache Spark, leveraging in-memory distributed computing. AI-driven systems rely on continuous real-time data. Fraud detection pipelines often process millions of events per second using Kafka for ingestion and Flink for processing. Without this infrastructure, AI models would miss critical patterns and signals. Data engineering must embed compliance directly into pipelines, especially under GDPR, CCPA and HIPAA. Core practices include: • Encryption (TLS, AES-256) for data in transit and at rest. • Anonymizing personally identifiable information (PII) before exposing data to models. • Role-based access control (RBAC) to restrict unauthorized access. These are not optional—they're essential for lawful, ethical AI. AI is also transforming data engineering itself. Several trends are accelerating this shift: Traditionally, cleaning data has been a manual, time-consuming task. Today, AI tools like Google Cloud's Dataprep automate anomaly detection, deduplication and schema validation—freeing engineers to focus on higher-value work. Companies adopting these tools must invest in training staff and adjusting governance processes to trust AI-driven quality control. Machine learning models rely heavily on well-chosen features. In modern MLOps workflows, feature stores help to identify optimal features, reducing the time it takes to prepare datasets for training. The challenge here is ensuring explainability—if AI chooses the features, humans still need to understand why. Rather than static ETL jobs, AI is now used to predict peak data loads and automatically scale pipeline resources accordingly. This DataOps approach ensures efficiency, but it requires advanced observability tools to monitor and fine-tune. With the rise of IoT, more data is processed at the edge (closer to where it's generated). Companies are embedding lightweight AI models directly into sensors, allowing devices to filter, clean and analyze data before sending it to the cloud. However, this raises new challenges around distributed model management and ensuring consistent results across devices. AI is only as good as the data it learns from. That data doesn't magically arrive in the right shape. It takes skilled data engineers to design ingestion pipelines, enforce data quality, scale infrastructure and ensure compliance. Organizations that invest in strong data engineering today will have a competitive advantage in future AI innovation. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Yahoo
17-03-2025
- Business
- Yahoo
Data Science Interview Preparation Course 2025 - Google Apple Amazon Interview Questions MCQ Answers
Santa Clara, March 17, 2025 (GLOBE NEWSWIRE) -- Santa Clara, California - The AI talent race has intensified as companies strive to innovate and maintain a competitive edge. This competition has led to a significant increase in job postings seeking AI expertise, with nearly one in four new U.S. tech job listings now explicitly requiring such skills. This trend highlights the urgency for professionals to upskill and align their competencies with industry demands. For more information visit This surge in demand underscores the need for professionals adept in data science and machine learning. Interview Kickstart (IK) addresses this need through its Data Science course, which is designed to equip individuals with the skills required to excel in this competitive field. Interview Kickstart's Data Science course is meticulously crafted to meet these demands. The program begins with a solid foundation in Python programming, covering essentials such as data structures, object-oriented programming, and the use of libraries like NumPy and Pandas. A comprehensive understanding of mathematics is vital in data science. The Interview Kickstart's Data Science course delves into descriptive statistics, probability, statistical methods, sampling, and hypothesis testing. This mathematical rigor enables participants to perform robust data analyses and derive meaningful insights. Participants also learn techniques for data transformation, preprocessing, feature engineering, and dimensionality reduction. These skills are essential for preparing data for modeling and uncovering underlying patterns. Given the importance of data science in advanced AI/ML applications, another module introduces participants to neural networks, image processing, computer vision, and natural language processing. The Interview Kickstart's Data Science course includes training in big data analysis using tools like Apache Spark and cloud computing platforms. This knowledge equips participants to handle large datasets efficiently, a common requirement in data-driven organizations. Data visualization and storytelling are also emphasized, teaching participants to create compelling visualizations that effectively communicate data insights to stakeholders. To bridge the gap between theory and practice, the course also offers capstone projects that simulate real-world challenges. These projects provide hands-on experience, enabling participants to apply their learning to practical scenarios. Interview Kickstart's program also offers extensive interview preparation, including data structures and algorithms, behavioral interview training, and mock interviews with industry experts. This holistic approach ensures participants are well-prepared to navigate the rigorous interview processes of top tech companies. The Interview Kickstart Data Science Course is designed for aspiring data scientists, engineers, and professionals from non-technical backgrounds looking to transition into AI and ML roles. Taught by FAANG+ ML Engineers, the program provides a 360° learning experience, covering essential data science concepts, machine learning, and real-world applications. The course includes 1:1 coaching, technical mentoring, and homework assistance, ensuring personalized support. Participants work on a capstone project, gaining exposure to real-world machine learning challenges. To prepare for job interviews, the program offers dedicated interview prep modules and mock interviews with top-tier engineers. Additionally, it focuses on career skills development, including resume building, LinkedIn optimization, and behavioral training. With a structured approach to data science and hands-on experience, this course is ideal for junior professionals, recent graduates, and experienced engineers looking to advance in AI and ML careers. As AI continues to permeate various sectors, the demand for data science professionals is expected to grow. Interview Kickstart's Data Science course positions professionals to capitalize on these opportunities, providing them with the skills and confidence to succeed in the competitive tech job market. To lear more visit About Interview Kickstart Founded in 2014, Interview Kickstart is a premier upskilling platform empowering aspiring tech professionals to secure roles at FAANG and top tech companies. With a proven track record and over 20,000 successful learners, the platform stands out with its team of 700+ FAANG instructors, hiring managers and tech leads, who deliver a comprehensive curriculum, practical insights, and targeted interview prep strategies. Offering live classes, 100,000+ hours of pre-recorded video lessons, and 1:1 sessions, Interview Kickstart ensures flexible, in-depth learning along with personalized guidance for resume building and LinkedIn profile optimization. The holistic support, spanning 6 to 10 months with mock interviews, ongoing mentorship, and industry-aligned projects, equips learners to excel in technical interviews and on the job. ### For more information about Interview Kickstart, contact the company here:Interview KickstartBurhanuddin Pithawala+1 (209) 899-1463aiml@ Patrick Henry Dr Bldg 25, Santa Clara, CA 95054, United States CONTACT: Burhanuddin PithawalaSign in to access your portfolio
