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IBM C2090-102 Practice Test Questions, Exam Dumps
IBM C2090-102 (IBM Big Data Architect) exam dumps vce, practice test questions, study guide & video training course to study and pass quickly and easily. IBM C2090-102 IBM Big Data Architect exam dumps & practice test questions and answers. You need avanset vce exam simulator in order to study the IBM C2090-102 certification exam dumps & IBM C2090-102 practice test questions in vce format.
The journey to becoming an IBM Certified Big Data Engineer begins with a thorough understanding of the fundamental principles that govern the world of big data. The C2090-102 Exam is designed to validate the skills and knowledge required to design, develop, and manage big data solutions. It is a comprehensive test that covers the entire lifecycle of data, from ingestion and storage to processing and analysis. Passing this exam signifies that a professional has the requisite expertise to handle the challenges and opportunities presented by massive datasets in a modern enterprise environment.
This first part of our series will lay the essential groundwork for your C2090-102 Exam preparation. We will start by defining what "big data" truly means, moving beyond the buzzword to understand its core characteristics. We will then explore the origins and architecture of Apache Hadoop, the open-source framework that revolutionized data processing. We will dissect its key components, including the Hadoop Distributed File System (HDFS) and the Yet Another Resource Negotiator (YARN). By the end of this section, you will have a strong foundational knowledge, which is the first critical step toward success.
To prepare for the C2090-102 Exam, you must first understand the problem that big data technologies were created to solve. The term "big data" is often defined by three core characteristics, commonly known as the Three Vs. The first is Volume, which refers to the sheer scale of data being generated. We are talking about terabytes, petabytes, and even exabytes of information, far beyond the capacity of traditional database systems. This massive volume necessitates distributed storage and processing systems to be managed effectively.
The second V is Velocity, which describes the speed at which new data is created and needs to be processed. Think of social media feeds, sensor data from IoT devices, or financial trading information. This data often arrives in real-time streams, requiring systems that can ingest and analyze it on the fly to derive timely insights. The third V is Variety, referring to the diverse range of data formats. Big data includes structured data from relational databases, semi-structured data like JSON and XML files, and unstructured data such as text documents, images, and videos. The C2090-102 Exam will test your understanding of these core concepts.
The C2090-102 Exam is deeply rooted in the Apache Hadoop ecosystem. Hadoop was created to address the challenges of storing and processing big data in a scalable and cost-effective way. Its core principle is to distribute both data and computation across clusters of commodity hardware. Instead of using one massive, expensive supercomputer, Hadoop allows you to chain together many standard servers, each with its own storage and processing power. This architecture provides massive parallel processing capabilities and is inherently fault-tolerant, as the failure of a single node does not bring down the entire system.
The Hadoop framework is fundamentally composed of two main pillars. The first is a distributed storage system, and the second is a distributed processing framework. Originally, these roles were filled by the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing. Over time, the ecosystem has evolved, with YARN taking over resource management duties from MapReduce, allowing other processing frameworks like Apache Spark to run on Hadoop. A solid grasp of this core architecture is a prerequisite for tackling the more advanced topics in the C2090-102 Exam.
HDFS is the primary storage system used by Hadoop, and its architecture is a critical topic for the C2090-102 Exam. It is a distributed file system designed to store very large files across multiple machines. HDFS achieves reliability by replicating data. When you store a file in HDFS, it is broken down into large blocks (typically 128MB or 256MB). Each of these blocks is then replicated and stored on several different machines in the cluster, usually three by default. This ensures that even if a machine or its disk fails, the data is still available from the other replicas.
The HDFS architecture follows a master-slave model. The master node is called the NameNode. The NameNode is responsible for managing the file system's metadata. It keeps track of which blocks make up a file and where those blocks are physically stored across the cluster. The slave nodes are called DataNodes. The DataNodes are the workhorses that actually store the data blocks and serve them to clients upon request from the NameNode. Understanding the distinct roles of the NameNode and DataNodes is essential for the C2090-102 Exam.
In the early days of Hadoop, MapReduce was responsible for both processing the data and managing the cluster's resources. This created limitations. YARN was introduced in Hadoop 2.0 to decouple these two functions, and it is a key subject for the C2090-102 Exam. YARN is effectively the operating system for the Hadoop cluster. Its sole purpose is to manage and allocate the cluster's resources, such as CPU and memory, to the various applications that need to run. This separation allows multiple different data processing frameworks, not just MapReduce, to run simultaneously on the same Hadoop cluster.
YARN also has a master-slave architecture. The master is the ResourceManager, which has a global view of all the resources in the cluster. When a client submits an application, the ResourceManager allocates the initial resources for it. On each slave node, there is a NodeManager, which is responsible for managing the resources on that specific machine and reporting its status back to the ResourceManager. When an application runs, it gets its own temporary manager called the ApplicationMaster, which negotiates for further resources from the ResourceManager as needed.
MapReduce is the original processing framework for Hadoop, and understanding its paradigm is crucial for the C2090-102 Exam. It is a model for processing large datasets in a parallel and distributed manner. A MapReduce job is broken down into two main phases: the Map phase and the Reduce phase. The core idea is to process the data where it is stored, on the DataNodes, rather than moving the data to a central processing unit. This minimizes network traffic and is key to Hadoop's scalability.
In the Map phase, the input data is broken down into chunks, and a "mapper" function is applied to each chunk in parallel across the cluster. The mapper's job is to process the data and emit intermediate key-value pairs. After the map phase, the framework sorts and shuffles these intermediate pairs, grouping all the values with the same key together. In the Reduce phase, a "reducer" function is applied to each of these groups of values, producing the final output. The classic "word count" example is the most common way to illustrate this two-stage process.
The C2090-102 Exam is titled "IBM Big Data Engineer." It is important to understand what this role entails. A big data engineer is responsible for building and maintaining the infrastructure and pipelines that allow for the large-scale processing of data. They are the architects of the data ecosystem. Their responsibilities include setting up and managing the Hadoop cluster, designing and building data ingestion pipelines to collect data from various sources, and creating data processing jobs to transform and clean the raw data into a usable format for analysis.
A big data engineer works closely with data scientists and data analysts. While the data scientist is focused on building machine learning models and finding insights, the big data engineer is the one who provides them with clean, reliable data in an efficient manner. They need a deep understanding of the tools in the Hadoop ecosystem, strong programming skills (often in Java, Scala, or Python), and a solid grasp of distributed systems concepts. The C2090-102 Exam is designed to validate these exact skills.
Data Ingestion and Storage
The first and most critical step in any big data workflow is getting data into the system. This process, known as data ingestion, involves collecting raw data from a multitude of sources and loading it into a distributed storage system like HDFS. The C2090-102 Exam places significant emphasis on a data engineer's ability to use the right tools for this job. The choice of tool and strategy depends heavily on the characteristics of the data source, whether it is a structured relational database, a stream of log files, or real-time sensor data.
This part of our series will provide a deep dive into the most common data ingestion tools and storage technologies covered by the C2090-102 Exam. We will explore Apache Sqoop for bulk data transfer from relational databases and Apache Flume for collecting and aggregating streaming data. We will then shift our focus to storage, examining the different file formats used in Hadoop to optimize performance and space. Finally, we will introduce Apache HBase, the non-relational NoSQL database of the Hadoop ecosystem, designed for real-time random access to massive datasets.
A vast amount of valuable enterprise data resides in traditional relational database management systems (RDBMS) like MySQL, Oracle, and DB2. Apache Sqoop is the go-to tool in the Hadoop ecosystem for efficiently transferring this structured data into HDFS. Understanding Sqoop's functionality is a key objective for the C2090-102 Exam. Sqoop works by connecting to the relational database, inspecting its schema, and then launching a MapReduce job to import or export the data in a parallel and fault-tolerant manner.
The sqoop import command is used to pull data from a database table into HDFS. Sqoop automatically manages the parallelization, dividing the import task among multiple mappers to speed up the transfer. Conversely, the sqoop export command is used to take data that has been processed in HDFS and load it back into a relational database. Sqoop provides a wide range of options to control the import process, such as specifying the file format, using WHERE clauses to filter data, and performing incremental imports to only fetch new or updated records.
While Sqoop excels at handling structured data in bulk, a different tool is needed for collecting and aggregating large volumes of streaming data, such as log files, social media feeds, or IoT sensor readings. This is the role of Apache Flume, a distributed and reliable service for efficiently moving this type of data. Flume's architecture and configuration are important topics for the C2090-102 Exam. Flume is designed to be highly configurable and robust, ensuring data is delivered even in the face of failures.
A Flume deployment, known as an agent, is built from three core components. The Source is the component that receives the data, either by listening on a network port, tailing a log file, or connecting to another system. The Channel is a temporary storage buffer that holds the data after it has been received by the source. Channels can be memory-based for speed or file-based for durability. The Sink is the component that reads data from the channel and writes it to its final destination, which is typically HDFS. Multiple agents can be chained together to create complex data flow pipelines.
Once data is ingested, how it is stored in HDFS can have a massive impact on both storage efficiency and query performance. The C2090-102 Exam will expect you to be familiar with the most common Hadoop file formats and their trade-offs. By default, data might be stored as simple text files, which are easy to read but are not efficient for storage or processing. To address this, several optimized, binary file formats have been developed. These formats are designed to be compressed, splittable for parallel processing, and often contain rich schema information.
SequenceFiles are a basic splittable binary format that stores data as key-value pairs. However, more advanced formats have largely superseded them. The choice of file format is a critical design decision for a big data engineer. Using the right format can dramatically reduce storage costs and speed up data analysis jobs. It is essential to understand the characteristics of each and when to use them, a key piece of knowledge for anyone preparing for the C2090-102 Exam.
Hadoop file formats can be broadly categorized into two types: row-based and columnar. This distinction is a fundamental concept for the C2090-102 Exam. Apache Avro is a popular row-based format. In an Avro file, all the data for a single record or row is stored together sequentially. Avro is an excellent choice for write-heavy workloads because new records can be quickly appended to the end of the file. It also has a strong schema evolution capability, meaning you can easily add, remove, or modify fields in your schema over time without having to rewrite old data files.
In contrast, Apache Parquet and Apache ORC (Optimized Row Columnar) are columnar formats. In these formats, the data is not stored by row, but by column. All the values for a single column are stored together contiguously on disk. This approach is incredibly efficient for read-heavy, analytical queries that only need to access a subset of the columns in a table. Because the query engine does not have to read through all the other columns it does not need, I/O is drastically reduced. These formats also offer excellent compression because data within a single column is often very similar and highly compressible.
While HDFS is optimized for storing large files and performing sequential reads for batch processing, it is not well-suited for low-latency, random read-and-write access. For use cases that require real-time lookups on massive datasets, the Hadoop ecosystem provides Apache HBase. HBase is a NoSQL, distributed database that runs on top of HDFS. It is a critical component of the Hadoop ecosystem and a key topic for the C2090-102 Exam. It is modeled after Google's Bigtable and is designed to host billions of rows and millions of columns.
HBase provides a very different data model from a traditional relational database. It is often described as a sparse, distributed, persistent, multi-dimensional sorted map. It is a key-value store where the values are indexed by a row key. This allows for incredibly fast lookups if you know the row key of the data you want to retrieve. It is an ideal solution for applications that need to serve data to users in real-time or for storing the output of analytical jobs for quick access.
To effectively use HBase, you must understand its architecture, a topic covered in the C2090-102 Exam. HBase also uses a master-slave architecture. The HBase Master is responsible for managing the cluster, including assigning data regions to servers and handling schema changes. The data itself is stored and served by RegionServers. The data in an HBase table is horizontally partitioned into "regions." Each RegionServer is responsible for managing one or more of these regions. This distributed nature is what allows HBase to scale horizontally.
The HBase data model is based on a few key concepts. All data is stored in tables. Each table has rows, and each row has a unique row key. Rows are made up of one or more column families, which are groups of related columns. Within a column family, you can have a very large number of individual columns, which are called column qualifiers. Each cell, defined by the intersection of a row key and a column, can have multiple versions, which are timestamped. This flexible, schema-on-read model is very different from the rigid schema of an RDBMS and is a core concept for the C2090-102 Exam.
After successfully ingesting and storing vast quantities of data within the Hadoop ecosystem, the next logical step is to process it. This is the heart of big data engineering: transforming raw, often messy data into a structured, clean, and valuable asset. The C2090-102 Exam thoroughly evaluates a candidate's understanding of the primary data processing frameworks available in Hadoop. This involves not only knowing the theoretical models but also understanding their practical applications, strengths, and weaknesses.
This third part of our series will focus on the core processing engines that power the Hadoop ecosystem. We will begin with a detailed look at the classic MapReduce paradigm, breaking down its phases and exploring its use cases. We will then transition to its modern successor, Apache Spark. We will uncover why Spark has become the de facto standard for big data processing, exploring its architecture, its use of in-memory computation for speed, and its core abstraction, the Resilient Distributed Dataset (RDD). This knowledge is absolutely critical for success on the C2090-102 Exam.
As discussed in Part 1, MapReduce is Hadoop's original processing model. A deep understanding of its mechanics is required for the C2090-102 Exam. Let's revisit the process with more detail. A MapReduce job processes data as key-value pairs. The input data is first split into manageable chunks. Each chunk is then fed to a Mapper task. The Mapper applies a user-defined function that processes the input and generates a set of intermediate key-value pairs. For a word count job, the mapper would take a line of text as input and output a key-value pair for each word, like (word, 1).
Following the Map phase is the critical Shuffle and Sort phase, which is handled automatically by the framework. The intermediate key-value pairs from all the mappers are collected, sorted by key, and then grouped. All values associated with the same key are brought together in a list. This list is then passed to a Reducer task. The Reducer applies a second user-defined function that aggregates the values in the list. For word count, the reducer would receive a key like (Hadoop, [1, 1, 1]) and would sum the list of ones to produce the final output: (Hadoop, 3).
While MapReduce was revolutionary, it has several limitations that are important to understand for the C2090-102 Exam. Its primary drawback is its performance, which is largely due to its heavy reliance on disk I/O. After the map phase, the intermediate data is written to the local disks of the DataNodes. During the shuffle phase, this data is then read from disk and sent over the network to the reducers, who may also write their output back to disk. This constant reading and writing makes MapReduce relatively slow, especially for iterative algorithms that require multiple passes over the data.
Furthermore, the MapReduce programming model can be quite rigid and verbose. Developers often have to write a significant amount of boilerplate code in Java to create even simple jobs. While this provides a lot of control, it is not ideal for rapid, exploratory data analysis. These challenges led to the development of higher-level abstraction tools like Hive and Pig, and ultimately to a completely new and more efficient processing engine: Apache Spark.
Apache Spark is a fast, general-purpose cluster computing system, and it is a major focus of the C2090-102 Exam. Spark was designed to overcome the limitations of MapReduce. Its key innovation is its ability to perform in-memory computing. Instead of writing intermediate data to disk after every operation, Spark can keep data in the cluster's collective RAM. This dramatically reduces the time spent on disk I/O and makes Spark up to 100 times faster than MapReduce for certain applications, particularly those involving iterative machine learning algorithms or interactive data analysis.
Spark is also much more flexible than MapReduce. It provides a richer set of APIs and supports multiple programming languages, including Scala, Java, Python, and R, making it accessible to a wider range of developers and data scientists. The Spark ecosystem also includes several powerful libraries for specific tasks, such as Spark SQL for working with structured data, Spark Streaming for real-time data processing, and MLlib for machine learning. This unified platform approach simplifies the development of complex, multi-stage data pipelines.
To pass the C2090-102 Exam, you must be familiar with Spark's architecture. A Spark application runs as a set of independent processes on a cluster, coordinated by a central SparkContext object in your main program, which is called the driver program. The driver is responsible for creating the SparkContext, which connects to a cluster manager like YARN. The cluster manager then allocates resources for your application by launching executor processes on the worker nodes of the cluster.
The executors are the processes that actually run the computations and store the data for your application. The driver program sends the application code and tasks to the executors to be run. The core abstraction in Spark is the Resilient Distributed Dataset, or RDD. An RDD is an immutable, partitioned collection of objects that can be operated on in parallel. It is the fundamental data structure in Spark, and all operations are performed on RDDs. Understanding the relationship between the driver, executors, and RDDs is central to understanding how Spark works.
The foundation of Spark is the Spark Core engine, which is responsible for task scheduling, memory management, and fault recovery. The primary data abstraction in Spark Core is the RDD, a concept you must master for the C2090-102 Exam. An RDD is fault-tolerant because Spark always keeps track of the lineage of an RDD, which is the complete sequence of transformations that were used to create it from its original source. If a partition of an RDD is lost due to a node failure, Spark can automatically recompute it using this lineage information.
There are two types of operations you can perform on RDDs: transformations and actions. Transformations are lazy operations that create a new RDD from an existing one. Examples include map(), filter(), and join(). Because they are lazy, transformations are not executed immediately; they are just added to the lineage graph. Actions are operations that trigger a computation and return a result to the driver program or write data to storage. Examples include count(), collect(), and saveAsTextFile(). A Spark job is not launched until an action is called.
While RDDs are powerful, working with them directly can sometimes be complex. For working with structured or semi-structured data, the Spark ecosystem provides a higher-level library called Spark SQL. This is an extremely important topic for the C2090-102 Exam. Spark SQL allows you to query structured data using standard SQL queries or through a DataFrame API. A DataFrame is a new data abstraction that is conceptually equivalent to a table in a relational database or a data frame in R or Python, but with richer optimizations.
DataFrames can be created from a wide variety of sources, such as existing RDDs, Hive tables, or structured data files like JSON or Parquet. Under the hood, Spark SQL uses a powerful optimizer called Catalyst to generate highly efficient physical execution plans for the queries. This often results in performance that is significantly better than what a developer could achieve by writing manual RDD operations. The familiar SQL interface and the performance benefits of the optimizer have made Spark SQL one of the most widely used components of Spark.
While powerful processing engines like MapReduce and Spark are essential for transforming large datasets, they often require specialized programming skills. To make big data more accessible to a broader audience, including data analysts, business intelligence professionals, and anyone comfortable with SQL, the Hadoop ecosystem includes several high-level abstraction tools. These tools provide simpler, more declarative interfaces for querying and analyzing data stored in HDFS. The C2090-102 Exam requires a solid understanding of these crucial components.
This fourth part of our series will explore the most important high-level data access tools in the Hadoop ecosystem. We will begin with Apache Hive, the project that first brought a SQL-like interface to Hadoop, effectively turning it into a data warehouse. We will then look at Apache Pig, a platform for creating data processing pipelines using a data flow scripting language. We will compare and contrast these two tools to understand their ideal use cases, providing the practical knowledge needed for success on the C2090-102 Exam.
Apache Hive is a data warehouse infrastructure built on top of Hadoop, and it is a cornerstone of the C2090-102 Exam curriculum. Its primary purpose is to provide an easy way to read, write, and manage large datasets residing in HDFS using a SQL-like query language called HiveQL. Hive takes a HiveQL query, validates it, and then translates it into an efficient series of MapReduce or Spark jobs that are executed on the Hadoop cluster. This allows users who are already familiar with SQL to analyze massive datasets without needing to write complex Java or Scala code.
Hive imposes a schema on data that is already stored in HDFS. It does not store the data itself; it simply stores the metadata about the data's structure and location. This is often referred to as "schema-on-read." You can define a table in Hive that maps to a directory of files in HDFS, and then you can query that data as if it were a traditional database table. This ability to provide structure to unstructured or semi-structured data is one of Hive's most powerful features.
To pass the C2090-102 Exam, you must understand the key components of the Hive architecture. When a user submits a HiveQL query, it first goes to the Hive Driver. The Driver manages the lifecycle of the query and interacts with the other components. It sends the query to a Compiler, which parses the query, performs a semantic analysis, and generates an execution plan. The execution plan is a directed acyclic graph (DAG) of stages, where each stage is typically a MapReduce or Spark job.
The most critical component of the Hive architecture is the Metastore. The Metastore is a relational database (often MySQL or PostgreSQL) that stores all the metadata for Hive. This includes information about databases, tables (like column names and data types), partitions, and the location of the actual data files in HDFS. The Driver consults the Metastore to validate the query against the table schemas. The execution engine then uses this metadata to find and read the correct data from HDFS.
HiveQL is the query language used by Hive. A major objective of the C2090-102 Exam is to test your familiarity with its syntax and capabilities. For anyone with a background in SQL, HiveQL will feel very familiar. It supports many of the standard SQL features, including SELECT, FROM, WHERE, GROUP BY, ORDER BY, and JOIN clauses. This allows analysts to perform complex data aggregations, filtering, and joins across very large datasets using a language they already know.
However, there are some differences and extensions. HiveQL includes features specifically designed for big data, such as support for different file formats and complex data types like arrays, maps, and structs. A particularly important feature is partitioning. You can partition a Hive table based on the values in one or more columns, such as by date. When you query a partitioned table and include a filter on the partition key in your WHERE clause, Hive is smart enough to only scan the data in the relevant partitions, dramatically improving query performance by avoiding a full table scan.
While Hive is excellent for data warehousing and SQL-based analysis, it is not always the best fit for all data processing tasks, especially complex Extract, Transform, and Load (ETL) pipelines. For these use cases, the Hadoop ecosystem offers Apache Pig. Pig is a platform for analyzing large datasets that consists of a high-level language for expressing data analysis programs, coupled with an execution engine that can run these programs on Hadoop. Your knowledge of Pig and its scripting language will be assessed on the C2090-102 Exam.
The language used by Pig is called Pig Latin. Pig Latin is not a query language like SQL; it is a data flow language. A Pig Latin script is a step-by-step description of how data should be loaded, processed, transformed, and stored. Each step in the script applies an operation to the data, such as loading, filtering, grouping, or joining, and produces a new dataset. This procedural, step-by-step approach makes Pig very well-suited for building complex, multi-stage data pipelines where the logic is not easily expressed in a single SQL query.
A Pig Latin script is a series of transformations applied to a dataset. Understanding this flow is a key part of preparing for the C2090-102 Exam. The script typically starts with a LOAD statement, which reads data from a source, such as a file in HDFS. The script then consists of a series of statements that transform the data. Each statement creates a new "relation," which is Pig's term for a dataset. Common transformation operators include FILTER to remove unwanted rows, FOREACH to apply an operation to each row, GROUP to collect rows with the same key, and JOIN to combine two relations.
Finally, the script will usually end with a STORE or DUMP statement. The STORE statement writes the final processed relation back to a file in HDFS. The DUMP statement simply prints the output to the screen, which is useful for debugging. Like Hive, Pig is lazy. It reads the entire script, builds a logical plan, optimizes it, and then compiles it into a physical plan of MapReduce or Spark jobs. The jobs are only executed when a STORE or DUMP command is encountered.
The C2090-102 Exam will expect you to know when to use Hive and when to use Pig. While both tools are used for data processing on Hadoop, they are designed for different use cases and different types of users. Hive, with its SQL-like interface, is primarily aimed at data analysts and BI professionals who need to run ad-hoc queries and perform data warehousing tasks. It provides a declarative interface where you specify what data you want, and Hive figures out how to get it.
Pig, on the other hand, is aimed more at programmers and data engineers who need to build complex data pipelines. Its procedural, data flow language gives developers more fine-grained control over the execution of the pipeline. It is excellent for ETL jobs that involve significant data cleaning, transformation, and pre-processing before the data is ready for analysis. In many organizations, it is common to see both tools used: Pig for the heavy-lifting ETL work, and Hive to provide a clean, queryable interface over the data that Pig has produced.
Having explored the core components of the Hadoop ecosystem, from storage and ingestion to processing and querying, we now arrive at the final set of topics crucial for the C2090-102 Exam. A real-world big data environment is not just a collection of independent tools; it is a cohesive system where complex, multi-stage data pipelines are orchestrated, monitored, and secured. A certified big data engineer must be proficient in managing these operational aspects to ensure the reliability and integrity of the data platform.
This concluding part of our series will focus on these advanced operational topics. We will discuss Apache Oozie, the workflow scheduler used to manage complex data pipelines. We will cover the fundamentals of securing a Hadoop cluster using Kerberos. We will also touch on cluster management tools like Apache Ambari. Finally, we will synthesize all the knowledge from this series into a targeted review and provide effective strategies for tackling the C2090-102 Exam, ensuring you are fully prepared for success.
Real-world data processing is rarely a single-step job. It is often a complex pipeline involving multiple sequential and parallel tasks. For example, a daily pipeline might involve a Sqoop job to ingest data, several Pig or Spark jobs to transform it, and finally a Hive job to update a summary table. Managing the dependencies and execution of these tasks manually is impractical and error-prone. Apache Oozie is a workflow scheduler system designed to manage and coordinate these Hadoop jobs. An understanding of Oozie is a key competency for the C2090-102 Exam.
Oozie allows you to define a Directed Acyclic Graph (DAG) of actions. You can specify that a particular Hive job should only run after a Spark job has successfully completed, or that two Pig jobs can run in parallel. Oozie manages the entire lifecycle of the workflow, submitting the jobs to YARN at the appropriate time, monitoring their status, and handling failures. This provides a robust and automated way to manage complex data pipelines.
An Oozie workflow is defined in an XML file called workflow.xml. This is a topic that may appear on the C2090-102 Exam. The workflow definition consists of control flow nodes and action nodes. Control flow nodes, such as start, end, fork, join, and decision, define the path of execution. For example, a fork node can be used to start multiple actions in parallel, and a join node will wait for all the parallel paths to complete before proceeding.
Action nodes are where the actual work is done. Oozie has built-in support for various action types, including MapReduce, Spark, Pig, Hive, and Sqoop. For each action, you specify the necessary information, such as the script to run, the required files, and any parameters. In addition to on-demand workflows, Oozie has a Coordinator component that allows you to schedule workflows to run at regular intervals (e.g., every day at midnight) or based on data availability (e.g., run when a new set of input files appears in an HDFS directory).
In a default Hadoop installation, security is minimal. Any user who can connect to the cluster can impersonate any other user and access any data. In a production enterprise environment, this is unacceptable. Securing a Hadoop cluster is a critical task, and the C2090-102 Exam requires you to understand the primary mechanism for doing so: Kerberos. Kerberos is a network authentication protocol that provides strong authentication for client/server applications by using secret-key cryptography.
When Kerberos is enabled on a Hadoop cluster, every user and every service (like the NameNode and ResourceManager) has a unique identity called a principal. To access a service, a user must first authenticate with a central Kerberos server, known as the Key Distribution Center (KDC). The KDC will grant the user a ticket. The user then presents this ticket to the Hadoop service they want to access. The service can then verify the ticket with the KDC to confirm the user's identity. This ensures that all interactions within the cluster are securely authenticated.
Installing, managing, and monitoring a large Hadoop cluster with dozens or hundreds of nodes can be an incredibly complex task. To simplify this, the ecosystem includes cluster management tools like Apache Ambari. While the C2090-102 Exam is tool-agnostic to some degree, understanding the function of a management tool like Ambari is important. Ambari provides an intuitive web-based user interface for provisioning, managing, and monitoring Hadoop clusters.
With Ambari, you can use a step-by-step wizard to install Hadoop and its related projects, like Hive, Pig, and Spark, across your entire cluster. It handles the distribution of software, the configuration of all the services, and the starting and stopping of the cluster. Once the cluster is running, Ambari provides a centralized dashboard for monitoring the health and performance of all the services. It displays key metrics, sends alerts when problems are detected, and simplifies common administrative tasks, making the life of a Hadoop administrator much easier.
As you prepare for the C2090-102 Exam, it is essential to conduct a final review of the most critical topics. Ensure you have a rock-solid understanding of the HDFS and YARN architectures. Be able to clearly explain the roles of the NameNode, DataNode, ResourceManager, and NodeManager. Review the data ingestion tools, Sqoop and Flume, and be able to articulate their primary use cases. Master the differences between row-based file formats like Avro and columnar formats like Parquet and ORC.
Dedicate significant time to the processing frameworks. Be able to walk through a MapReduce job and explain the Map and Reduce phases. For Spark, make sure you understand the concepts of the driver, executors, RDDs, lazy evaluation, transformations, and actions. For the high-level tools, be confident in your ability to write a basic HiveQL query and a simple Pig Latin script. Finally, review the purpose of Oozie for workflow orchestration and Kerberos for security.
On the day of the C2090-102 Exam, having a good test-taking strategy is as important as your technical knowledge. First, manage your time wisely. Read each question carefully to ensure you understand what is being asked before looking at the options. If you encounter a difficult question, do not spend too much time on it. Make your best educated guess, mark the question for review, and move on. You can come back to it later if you have time at the end. This ensures you get to attempt every question.
Pay close attention to keywords in the question, such as "most efficient," "best," or "primary." These can provide clues to the correct answer. Use the process of elimination to narrow down the choices. Often, you can immediately identify one or two options that are clearly incorrect, which increases your chances of selecting the right answer from the remaining choices. Stay calm, trust in your preparation, and read each question methodically. Good luck!
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