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Components of a Big Data Pipeline Data Sources (Collection): Data originates from various sources, such as databases, APIs, and log files. Examples include transactional databases, social media feeds, and IoT sensors. This phase ensures quality and consistency using frameworks like Apache Spark or AWS Glue.
This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). In-Memory Databases: Databases such as Redis store data in memory for lightning-fast access and processing speeds. Variety Variety indicates the different types of data being generated.
This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). In-Memory Databases: Databases such as Redis store data in memory for lightning-fast access and processing speeds. Variety Variety indicates the different types of data being generated.
They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes. Data Modelling Data modelling is creating a visual representation of a system or database. Physical Models: These models specify how data will be physically stored in databases.
Data can come from different sources, such as databases or directly from users, with additional sources, including platforms like GitHub, Notion, or S3 buckets. Vector Databases Vector databases help store unstructured data by storing the actual data and its vector representation. mp4,webm, etc.), and audio files (.wav,mp3,acc,
Below are some prominent use cases for Apache NiFi: Data Ingestion from Diverse Sources NiFi excels at collecting data from various sources, including log files, sensors, databases, and APIs. It can connect to various database s, file systems, and cloud storage solutions, enabling seamless data transfer without significant downtime.
Setting up a Hadoop cluster involves the following steps: Hardware Selection Choose the appropriate hardware for the master node and worker nodes, considering factors such as CPU, memory, storage, and network bandwidth. ApacheHadoop, Cloudera, Hortonworks). Download and extract the ApacheHadoop distribution on all nodes.
It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database. The events can be published to a message broker such as ApacheKafka or Google Cloud Pub/Sub.
Best Big Data Tools Popular tools such as ApacheHadoop, Apache Spark, ApacheKafka, and Apache Storm enable businesses to store, process, and analyse data efficiently. Real-Time Data Analysis: Connects seamlessly with various databases for live analysis.
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