Big data principles and best practices pdf download

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You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.

This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems.

James Warren is an analytics architect with a background in machine learning and scientific computing. About the Author Nathan Marz is currently working on a new startup. Previously, he was the lead engineer at BackType before being acquired by Twitter in At Twitter, he started the streaming compute team which provides and develops shared infrastructure to support many critical realtime applications throughout the company. Nathan is the creator of Cascalog and Storm, open-source projects which are relied upon by over 50 companies around the world, including Yahoo!

James Warren is an analytics architect at Storm8 with a background in big data processing, machine learning and scientific computing. Other books in this area tend to focus a lot more on the "gee whiz" coolness of data science and machine learning applications By Kirk D. Borne I have rarely seen a thorough discussion of the importance of data modeling, data layers, data processing requirements analysis, and data architecture and storage implementation issues along with other "traditional" database concepts in the context of big data.

This book delivers a refreshing comprehensive solution to that deficiency. Other books in this area tend to focus a lot more on the "gee whiz" coolness of data science and machine learning applications which are aspects of big data that I happen to love, but they are not the whole story. This website is available with pay and free online books. You can start in searching the book in titled Big Data: Principles and best practices of scalable realtime data systems in the search menu.

Then download it. Stall for certain the minutes until the implement is coating. This padded register is sincere to analyse as soon as you desire. Menu Home. Newer Post Older Post Home. Ricette per sopravvivere lon Read Support Constructing the batch views requires computing functions on the entire for parallel master dataset. The batch storage must consequently support parallel pro- processing cessing to handle large amounts of data in a scalable manner.

Both Tunable Storage costs money. You may choose to compress your data to help mini- storage and mize your expenses, but decompressing your data during computations can processing affect performance. The batch layer should give you the flexibility to decide costs how to store and compress your data to suit your specific needs. The best you can do is put checks in place to disallow mutable operations. These checks should prevent bugs or other random errors from trampling over existing data.

With such loose requirements—not even needing random access to the data—it seems like you could use pretty much any distributed database for the master dataset.

The only really viable idea is to generate a UUID to use as a key. Files are sequences of bytes, and the most efficient way to consume them is by scan- ning through them.

You have full control over the bytes of a file, and you have the full freedom to compress them however you want. On top of that, filesystems implement fine-grained permissions systems, which are per- fect for enforcing immutability.

The problem with a regular filesystem is that it exists on just a single machine, so you can only scale to the storage limits and processing power of that one machine. They scale by adding more machines to the cluster. Distributed filesystems are designed so that you have fault tolerance when a machine goes down, meaning that if you lose one machine, all your files and data will still be accessible. There are some differences between distributed filesystems and regular filesys- tems.

The operations you can do with a distributed filesystem are often more limited than you can do with a regular filesystem. For instance, you may not be able to write to the middle of a file or even modify a file at all after creation. Oftentimes having small. We feel the design of HDFS is sufficiently representative of how distributed file- systems work to demonstrate how such a tool can be used for the batch layer.

HDFS and Hadoop MapReduce are the two prongs of the Hadoop project: a Java framework for distributed storage and distributed processing of large amounts of data. Hadoop is deployed across multiple servers, typically called a cluster, and HDFS is a distributed and scalable filesystem that manages how data is stored across the clus- ter.

In an HDFS cluster, there are two types of nodes: a single namenode and multiple datanodes. Each block is then replicated across multiple datanodes typically three that are chosen at random. The namenode keeps track of the file-to-block mapping and where each block is located. This design is shown in figure 4. Data file: b All typically large files are broken logs.

Namenode: logs. Client application. B When an application processes C Once the locations are known, a file stored in HDFS, it first the application contacts the queries the namenode for datanodes directly to access the block locations.

Distributing a file in this way across many nodes allows it to be easily processed in par- allel. When a program needs to access a file stored in HDFS, it contacts the namenode to determine which datanodes host the file contents. This process is illustrated in fig- ure 4. Additionally, with each block replicated across multiple nodes, your data remains available even when individual nodes are offline.

What you can do instead is spread the master dataset among many files, and store. Each file would contain many serialized data objects, as illustrated in figure 4. To append to the master dataset, you simply add a new file containing the new data objects to the master dataset folder, as is shown in figure 4. Serialized data object Upload. This is shown in table 4. Write Efficient Appending new data is as simple as adding a new file to the folder contain- appends of ing the master dataset.

Scalable Distributed filesystems evenly distribute the storage across a cluster of storage machines. Read Support Distributed filesystems spread all data across many machines, making it for parallel possible to parallelize the processing across many machines. Distributed processing filesystems typically integrate with computation frameworks like MapReduce to make that processing easy to do discussed in chapter 6. Both Tunable Just like regular filesystems, you have full control over how you store your storage and data units within the files.

You choose the file format for your data as well processing as the level of compression. To enforce immutability, you can dis- able the ability to modify or delete files in the master dataset folder for the user with which your application runs. This redundant check will protect your previously existing data against bugs or other human mistakes. At a high level, distributed filesystems are straightforward and a natural fit for the mas- ter dataset.

Of course, like any tool they have their quirks, and these are discussed in the following illustration chapter. For example, you may have a computa- tion that only requires information collected during the past two weeks.

The batch storage should allow you to partition your data so that a function only accesses data relevant to its computation. This process is called vertical partitioning, and it can greatly contribute to making the batch layer more efficient. Vertically partitioning data on a distributed filesystem can be done by sorting your data into separate folders. By sorting information for each date in separate folders, a function can select only the folders containing data relevant to its com- putation.

Each login contains a username, IP address, and timestamp. To vertically partition by day, you can create a separate folder for each day of data. Each day folder would have many files containing the logins for that day. This is illustrated in fig- ure 4. Now if you only want to look at a particular subset of your dataset, you can just look at the files in those particular folders and ignore the other files.

Suppose the data in the folders is contained in files, as shown in figure 4. Unfortunately, this code has serious problems. If the master dataset folder contains any files of the same name, then the mv operation will fail. To do it correctly, you have to be sure you rename the file to a random filename and so avoid conflicts. One of the core requirements of storage for the master dataset is the ability to tune the trade-offs between storage costs and processing costs. When storing a master dataset on a distributed filesystem, you choose a file format and compression format that makes the trade-off you desire.

All the operations and checks that need to happen to get these operations working correctly strongly indicate that files and folders are too low-level of an abstraction for manipulating datasets. When you last left this project, you had created a graph schema to represent the dataset. Every edge and property is represented via its own independent DataUnit. A key observation is that a graph schema provides a natural vertical partitioning of the data.

You can store all edge and property types in their own folders. Vertically par- titioning the data this way lets you efficiently run computations that only look at cer- tain properties and edges. You observed that these requirements could be mapped to a required checklist for a storage solution, and you saw that a distributed filesystem is a natural fit for this purpose. Using and applying a distributed filesystem should feel very familiar.

In the last chapter you saw the requirements for storing a master dataset and how a distributed filesystem is a great fit for those requirements.

But you also saw how using a filesystem API directly felt way too low-level for the kinds of operations you need to do on the master dataset. As always, our goal is not to compare and contrast all the possible tools but to reinforce the higher-level concepts. Getting started with Hadoop Setting up Hadoop can be an arduous task. Hadoop has numerous configuration parameters that should be tuned for your hardware to perform optimally.

To avoid get- ting bogged down in details, we recommend downloading a preconfigured virtual machine for your first encounter with Hadoop. At the time of this writing, Hadoop vendors Cloudera, Hortonworks, and MapR all have images publicly available.

We recommend having access to Hadoop so you can follow along with the examples in this and later chapters. Suppose you wanted to store all logins on a server. As we mentioned earlier, the file was automatically chunked into blocks and distrib- uted among the datanodes when it was uploaded. There can be an order of magnitude difference in performance between a MapReduce job that con- sumes 10 GB stored in many small files versus a job processing that same data stored in a few large files.

The reason is that a MapReduce job launches multiple tasks, one for each block in the input dataset. Each task requires some overhead to plan and coordinate its execu- tion, and because each small file requires a separate task, the cost is repeatedly incurred. One part of a solution being elegant is that it must be able to express the computations you care about in a concise manner.

As you saw in the last chapter, accomplishing these tasks with files and folders directly is tedious and error-prone. IOException; import backtype. With Pail, you can append folders in one line of code and consolidate small files in another.

When appending, if the data of the target folder is of a different file format, Pail will automatically coerce the new data to the correct file format. If the target folder has a different vertical partitioning scheme, Pail will throw an exception. Most importantly, a higher-level abstraction like Pail allows you to work with your data directly rather than using low-level containers like files and directories. Recall that the master dataset is the source of truth within the Lambda Architecture, and as such the batch layer must handle a large, growing data- set without fail.

Furthermore, there must be an easy and effective means of transform- ing the data into batch views to answer actual queries. This chapter is more technical than the previous ones, but always keep in mind how everything integrates within the Lambda Architecture.

This abstraction makes it significantly easier to manage a collection of records for batch processing. As the name suggests, Pail uses pails, folders that keep metadata about the dataset. By using this metadata, Pail allows. The goal of Pail is simply to make the operations you care about—appending to a dataset, vertical partitioning, and consolidation—safe, easy, and performant.

Why the focus on Pail? Pail, along with many other packages covered in this book, was written by Nathan while developing the Lambda Architecture. We introduce these technologies not to promote them, but to discuss the context of their origins and the problems they solve. Because Pail was developed by Nathan, it perfectly matches the requirements of the master dataset as laid out so far, and those requirements naturally emerge from the first principles of queries as a function of all data.

Feel free to use other libraries or to develop your own—our emphasis is to show a specific way to bridge the concepts of building Big Data systems with the available tooling. As you explore Pail, keep in mind how it preserves the advantages of HDFS while streamlining operations on the data.

The pailfile contains the records you just stored. These unique names allow multiple sources to write concurrently to the same pail without conflict. The following listing has a simplified class to represent a login. To store these Login objects in a pail, you need to create a class that implements the PailStructure interface. The next listing defines a LoginPailStructure that describes how serialization should be performed.

By passing this LoginPailStructure to the Pail create function, the resulting pail will use these serialization instructions. You can then give it Login objects directly, and Pail will handle the serialization automatically.

Likewise, when you read the data, Pail will deserialize the records for you. The operations are all implemented using MapReduce, so they scale regard- less of the amount of data in your pail, whether gigabytes or terabytes. In the previous section we discussed the importance of append and consolidate operations. The append operation is par- ticularly smart. If the pails store the same type of records but in different file formats, it coerces the data to match the format of the target pail.

This means the trade-off you decided on between storage costs and processing performance will be enforced for that pail. By default, the consolidate operation merges small files to create new files that are as close to MB as possible—a standard HDFS block size. This operation also paral- lelizes itself via MapReduce. For our logins example, suppose you had additional logins in a separate pail and wanted to merge the data into the original pail.

The major upstroke is that these built-in functions let you focus on what you want to do with your data rather than worry about how to manipulate files correctly. Imagine trying to manage the vertical partitioning manually.

Thankfully, Pail is smart about enforcing the structure of a pail and protects you from making these kinds of mistakes. Pail uses these methods to enforce its structure and automatically map records to their correct subdirectories. The following code demonstrates how to partition Login objects so that records are grouped by the login date.

You can control how Pail stores records in those files by specifying the file format Pail should be using. This lets you control the trade-off between the amount of storage space Pail uses and the per- formance of reading records from Pail. As discussed earlier in the chapter, this is a fundamental control you need to dial up or down to match your application needs. You can implement your own custom file format, but by default Pail uses Hadoop SequenceFiles.

This format is very widely used, allows an individual file to be pro- cessed in parallel via MapReduce, and has native support for compressing the records in the file.

Creates a new pail to store Login options with the desired format. This pail will use significantly less space but will have a higher CPU cost for reading and writing records. Table 5. Operation Criteria Discussion. Scalable storage The namenode holds the entire HDFS namespace in memory and can be taxed if the filesystem contains a vast number of small files. Read Support for paral- The number of tasks in a MapReduce job is determined by the num- lel processing ber of blocks in the dataset.

Consolidating the contents of a pail low- ers the number of required tasks and increases the efficiency of processing the data. Ability to vertically Output written into a pail is automatically partitioned with each fact partition data stored in its appropriate directory.

This directory structure is strictly enforced for all Pail operations. This coercion occurs automatically while perform- ing operations on the pail. Enforceable Because Pail is just a thin wrapper around files and folders, you can immutability enforce immutability, just as you can with HDFS directly, by setting the appropriate permissions. That concludes our whirlwind tour of Pail. Recall the Thrift schema we developed for SuperWebAnalytics.

Figure 5. Thrift serialization is independent of the type of data being stored, and the code is cleaner by separating this logic. What matters is that this code works for any graph schema, and it continues to work even as the schema evolves over time. The following listing demonstrates how to use Thrift utilities to serialize and deserialize your data. All of the following snippets are extracted from the SplitDataPailStructure class that accomplishes this task.

The next listing contains the code that generates the field map. It works for any graph schema, not just this example. As mentioned in the code annotation, FieldStructure is an interface shared by both PropertyStructure and EdgeStructure.

The SplitDataPailStructure is responsible for the top-level directory of the verti- cal partitioning, and it passes the responsibility of any additional subdirectories to the FieldStructure classes.

Therefore, once you define the EdgeStructure and PropertyStructure classes, your work will be done. Edges are structs and hence cannot be further partitioned.

But properties are unions, like the DataUnit class. The code similarly uses inspection to create a set of valid Thrift field IDs for the given property class.

For completeness we provide the full listing of the class here, but the key points are the construction of the set and the use of this set in fulfilling the FieldStructure contract.

The good news is that this was a one-time cost. You then were introduced to the Pail abstraction. Pail isolates you from the file for- mats and directory structure of HDFS, making it easy to do robust, enforced vertical partitioning and perform common operations on your dataset. Using the Pail abstrac- tion ultimately takes very few lines of code.

Vertical partitioning happens automati- cally, and tasks like appends and consolidation are simple one-liners. This means you can focus on how you want to process your records rather than on the details of how to store those records. The goal of a data system is to answer arbitrary questions about your data. Any question you could ask of your dataset can be implemented as a function that takes all of your data as input. Ideally, you could run these functions on the fly whenever you query your dataset.

Unfortunately, a function that uses your entire dataset as input will take a very long time to run. You need a different strategy if you want your queries answered quickly. In the Lambda Architecture, the batch layer precomputes the master dataset into batch views so that queries can be resolved with low latency. This requires strik- ing a balance between what will be precomputed and what will be computed at exe- cution time to complete the query.

By doing a little bit of computation on the fly to complete queries, you save yourself from needing to precompute absurdly large. The key is to precompute just enough information so that the query can be completed quickly.

In the last two chapters, you learned how to form a data model for your dataset and how to store your data in the batch layer in a scalable way. These queries illustrate the concepts of batch computation—each example shows how you would compute the query as a function that takes the entire master dataset as input.

The goal of the query is to determine the total number of pageviews of a URL for a range given in hours. To compute this query using a function of the entire dataset, you simply iterate through every record, and keep a counter of all the pageviews for that URL that fall within the specified range. After exhausting all the records, you then return the final value of the counter. The algorithm first performs semantic normalization on the names for the person, doing conversions like Bob to Robert and Bill to William.

The algorithm then makes use of a model that provides the probability of a gender for each name. The resulting inference algorithm looks like this:. An interesting aspect of this query is that the results can change as the name normal- ization algorithm and name-to-gender model improve over time, and not just when new data is received. The query determines an influencer score for each person in the social network.

The score is computed in two steps. First, the top influencer for each person is selected based on the number of reactions the influencer caused in that person. Otherwise, the algorithm simply counts the number of reactions between each pair of people and then counts the number of people for whom the queried user is the top influencer. When processing queries, each layer in the Lambda Architecture has a key, comple- mentary role, as shown in figure 6.

Processing the view entire dataset introduces high latency. Serving layer Realtime Batch Batch Batch view view view view. This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises.

Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries.

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers.

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Practice tips and war stories: 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research. Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions. Data gymnastics: over a dozen analytics patterns that your team will encounter again and again in projects The Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting Reproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutiny Practice tips and war stories: 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions Data gymnastics: over a dozen analytics patterns that your team will encounter again and again in projects.

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