What modules does it consist of? Apache hadoop is a software framework that allows for the distributed processing of large datasets across clusters of computers. It is designed to effectively scale from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer. The project includes these modules: Hadoop Distributed File system (hdfs a distributed file system that provides high-throughput access to application data. Hadoop MapReduce: A system for parallel processing of large datasets.
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In tuning for a specific machine, one may use a hybrid algorithm which uses blocking tuned for the specific cache sizes at the bottom level, but otherwise uses the cache-oblivious algorithm. The ability to perform well, independent of essay cache size and without cache-size-specific tuning, is the primary advantage of the cache oblivious approach. However, it is important to acknowledge that this lack of any cache-size-specific tuning also means that a cache oblivious algorithm may not perform as well as a cache-aware algorithm (i.e., an algorithm tuned to a specific cache size). Another disadvantage of the cache oblivious approach is that it typically increases the memory footprint of the data structure, which may further degrade performance. Interestingly though, in practice, performance of cache oblivious algorithms are booking often surprisingly comparable to that of cache aware algorithms, making them that much more interesting and relevant for big data processing. Big Data technologies These days, its not only about finding a single tool to get the job done; rather, its about building a scalable architecture to effectively collect, process, and query enormous volumes of data. Armed with a strong foundational knowledge of big data algorithms, techniques, and approaches, a big data expert will be able to employ tools from a growing landscape of technologies that can be used to exploit big data to extract actionable information. The questions that follow can help evaluate this dimension of a candidates expertise. Q: What is Hadoop? What are its key features?
Exploring a representative sample is easier, more efficient, and can in many you cases be nearly as accurate as exploring the entire dataset. The table below describes some of the statistical sampling techniques that are more commonly used with big data. Common sampling techniques technique description Advantages Disadvantages every element has the same chance of selection (as does any pair of elements, triple of elements, etc.) Minimizes bias Simplifies analysis of results Vulnerable to sampling error Orders data and selects elements at regular intervals through the. Discuss some of its advantages and disadvantages in the context of processing big data. A cache oblivious (a.k.a., cache transcendent) algorithm is designed to take advantage of a cpu cache without knowing its size. Its goal is to perform well without modification or tuning on machines with different cache sizes, or for a memory hierarchy whose levels are of different cache sizes. Typically, a cache oblivious algorithm employs a recursive divide and conquer approach, whereby the problem is divided into smaller and smaller sub-problems, until a sub-problem size is reached that fits into the available cache. For example, an optimal cache oblivious matrix multiplication is obtained by recursively dividing each matrix into four sub-matrices to be multiplied.
This exception, commonly known as disconnected life operation or offline mode, is becoming increasingly important. Some html5 feature make disconnected operation easier going forward. These systems normally choose a over c and thus must recover from long partitions. Q: What is dimensionality reduction and how is it relevant to processing big data? Name some techniques commonly employed for dimensionality reduction. Dimensionality reduction is the process of converting data of very high dimensionality into data of lower dimensionality, typically for purposes such as visualization (i.e, projection onto a 2D or 3D space for visualization purposes compression (for efficient storage and retrieval or noise removal. Some of the more common techniques for dimensionality reduction include: Note: Each of the techniques listed above is itself a complex topic, so each is provided as a hyperlink to further information for those interested in learning more. Q: Discuss some common statistical sampling techniques, including their strengths and weaknesses. When analyzing big data, processing the entire dataset would often be operationally untenable.
In nearly all models, elements like consistency and availability often are viewed as resource competitors, where adjusting one can impact another. Accordingly, the cap theorem (a.k.a. Brewers theorem) states that its impossible for a distributed computer system to provide more than two of the following three guarantees concurrently: c onsistency (all nodes see the same data at the same time) a vailability (a guarantee that every request receives a response about. The cap theorem has therefore certainly proven useful, fostering much discussion, debate, and creative approaches to addressing tradeoffs, some of which have even yielded new systems and technologies. Yet at the same time, the 2 out of 3 constraint does somewhat oversimplify the tensions between the three properties. By explicitly handling partitions, for example, designers can optimize consistency and availability, thereby achieving some trade-off of all three. Although designers do need to choose between consistency and availability when partitions are present, there is an incredible range of flexibility for handling partitions and recovering from them. Aspects of the cap theorem are often misunderstood, particularly the scope of availability and consistency, which can lead to undesirable results. If users cannot reach the service at all, there is no choice between c and a except when part of the service runs on the client.
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Many databases rely upon locking to provide acid capabilities. Locking means that the transaction marks the data that it accesses so that the dbms knows not to allow other transactions to modify it until the first transaction succeeds or fails. An alternative to locking is multiversion concurrency control in which the database provides each reading transaction the prior, unmodified version of report data that is being modified by another active transaction. Guaranteeing acid properties in a distributed transaction across a distributed database where no single node is responsible for all data affecting a transaction presents additional complications. Network connections might fail, or one node might successfully complete its part of the transaction and then be required to roll back its changes, because of a failure on another node. The two-phase commit protocol (not to be confused with two-phase locking) provides atomicity for distributed transactions to ensure that each participant in the transaction agrees on whether the transaction should be committed or not. In contrast to acid (and its immediate-consistency-centric approach base (Basically available, soft State, eventual Consistency) favors availability over consistency of operations.
Base was developed as an alternative for producing more scalable and affordable data architectures. Allowing less constantly updated data gives developers the freedom to build other efficiencies into the overall system. In base, engineers embrace the idea that data has the flexibility to be eventually updated, resolved or made consistent, rather than instantly resolved. The eventual Consistency model employed by base informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications.
Produces complex models for clusters that can also capture correlation and dependence of attributes Can suffer from overfitting (i.e., describing random error or noise instead of the underlying relationships) Requires selection of appropriate data models to optimize (which can be quite challenging for many real-world. Objects in sparse areas are usually considered to be noise and/or border points. Connects points based on distance thresholds (similar to linkage-based clustering but only connects those that satisfy a specified density criterion. Most popular density-based clustering method is dbscan, which features a well-defined cluster model called "density-reachability". Doesn't require specifying number of clusters a priori can find arbitrarily-shaped clusters; can even find a cluster completely surrounded by (but not connected to) a different cluster Mostly insensitive to the ordering of the points in the database Expects a density "drop" or "cliff".
Acid refers to the following set of properties that collectively guarantee reliable processing of database transactions, with the goal being immediate consistency: A tomicity. Requires each transaction to be all or nothing;. E., if one part of the transaction fails, the entire transaction fails, and the database state is left unchanged. Requires every transaction to bring the database from one valid state to another. Any data written to the database must be valid according to all defined rules, including (but not limited to) constraints, cascades, triggers, and any combination thereof. Requires concurrent execution of transactions to yield a system state identical to that which would be obtained if those same transactions were executed sequentially. Requires that, once a transaction has been committed, it will remain so even in the event of power loss, crashes, or errors.
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Complexity generally makes them too slow for large datasets "Chaining phenomenon whereby outliers either show up as additional clusters or cause other clusters to merge erroneously. Clusters are represented by a central vector, which is not necessarily a member of the essay set. When the number of clusters is fixed to k, k-means clustering gives a formal definition as an optimization problem: find the k cluster centers and assign the objects to the nearest cluster center, such that the squared distances from the cluster are minimized. With large number of variables, k-means may be computationally faster than hierarchical clustering (if k is small). K-means may produce tighter clusters than hierarchical clustering, especially if clusters are globular. Requires number of clusters (K) to be specified in advance. Prefers clusters of approximately similar size, which often leads to incorrectly set borders between clusters. Unable to represent density-based clusters, based on distribution models, clusters objects that appear to belong lab to the same distribution. Closely resembles the way artificial datasets are generated (i.e., by sampling random objects from a distribution).
Clustering algorithms can be logically categorized based on their underlying cluster model as summarized in the table below. Clustering algorithms, category, description, advantages, disadvantages, based on core idea of objects that are "close" to one another being more related, words these algorithms connect objects to form clusters based on distance. Employs a distance algorithm (such. Levenshtein distance in the case of string comparison) to determine "nearness" of objects. Linkage criteria can be based on minimum distance (single linkage maximum distance (complete linkage average distance, centroid distance, or any other algorithm of arbitrary complexity. Clustering can be agglomerative (starting with single elements, aggregating them into clusters) or divisive (starting with the complete dataset, dividing it into partitions). Does not require pre-specifying the number of clusters. Can be useful for proof-of-concept or preliminary analyses. Produce a hierarchy from which user still needs to choose appropriate clusters.
the stream the same probability of appearing in the output sample. Q: Describe and compare some of the more common algorithms and techniques for cluster analysis. Cluster analysis is a common unsupervised learning technique used in many fields. It has a huge range of applications both in science and in business. A few examples include: bioinformatics : Organizing genes into clusters by analyzing similarity of gene expression patterns. Marketing : Discovering distinct groups of customers and the using this knowledge to structure a campaign that targets the right marketing segments. Insurance : Identifying categories of insurance holders that have a high average claim cost.
As such, software engineers who do have expertise in these areas are both hard to find and extremely valuable to your team. The questions that follow can be helpful in gauging such expertise. Q: given a stream of data of unknown length, and a requirement to create a sample of a fixed size, how might you perform a simple random sample across the entire dataset? (i.e., given n elements in a data stream, how can you produce a sample of k elements, where n k, whereby every element has a 1/N chance of being included in the sample? One of the effective algorithms for addressing this is known. The basic procedure is as follows: Create an surgery array of size. Fill the array with the first k elements from the stream.
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Big data is an extremely broad domain, typically addressed by a hybrid team of data scientists, software engineers, and statisticians. Finding a single individual knowledgeable in the entire breadth of this domain is therefore extremely unlikely and rare. Rather, one will most likely be searching for multiple individuals with specific sub-areas of expertise. This guide is therefore divided at a high level into two sections: This guide highlights questions related to key concepts, paradigms, and technologies in which a big essay data expert can be expected to have proficiency. Bear in mind, though, that not every a candidate will be able to answer them all, nor does answering them all guarantee an A candidate. Ultimately, effective interviewing and hiring is as much of an art as it is a science. Big Data Algorithms, techniques, and Approaches. When it comes to big data, fundamental knowledge of relevant algorithms, techniques, and approaches is essential. Generally speaking, mastering these areas requires more time and skill than becoming an expert with a specific set of software languages or tools.