Hire Big Data Developers

Which Big Data technologies do you require?

One tool is hardly enough to collect, process, and query vast volumes of data. You need to build a scalable architecture. The landscape of technologies is continually growing. So, to harness the power of big data for your business, you would need expert engineers.

The task of finding them will be much easier with Data Science UA as your partner.

There are many Big Data approaches, algorithms, and techniques top professionals should master. The Big Data developers we can hire for you have all skills and experience required to use any of the key technologies:

Apache Hadoop

It’s a software framework, allowing clusters of computers to process large datasets. With its help, you can scale from one server to thousands, using their computation resources and storage. The library allows its user to detect and deal with failures at the application layer.

Various modules handle those tasks. Hadoop Distributed File System or HDFS provides high-efficiency access to application data. MapReduce is a system designed for the parallel processing of enormous datasets. With the help of the YARN framework, you can schedule jobs and manage cluster resources. You can find other utilities in Hadoop Common.

Here are some more details about the two key modules:

HDFS

HDFS is a fault-tolerant filesystem. It’s typically used as a source and output of data in Hadoop jobs. HDFS has a write-once-read-many type of data access. Therefore, information, once written, can’t be modified, but appending data is possible. Directories and files can be organized in traditional hierarchies.

Each HDFS cluster has a so-called NameNod to manage the file system namespace and file access. Several DataNodes regulate storage attached to the nodes. Technically, a file can be split into several blocks to be stored in a set of DataNodes.

Therefore, the NameNode is responsible for opening, closing, and renaming directories and files. Besides, it holds the map of file blocks. The DataNodes allows the file system’s clients to perform read and write operations.

MapReduce

MapReduce serves to process and generate large datasets. A MapReduce program works in two phases: ‘Map’ splits and maps data, ‘Reduce’ shuffles and shrinks it. A MapReduce system manages the process by commanding the servers, launching parallel tasks, controlling all data transfers between the different parts of the system.

The critical role of the MapReduce framework is to ensure scalability and fault-tolerance. But using it in a single-thread mode is unlikely to yield better processing speed. Optimized distributed shuffle operation reduces network communication costs, so fault-tolerant features of the framework become prominent.

The parallelism in Mapreduce facilitates recovery from a partial failure of servers. Multi-machine clusters are likely to face problems with single nodes. With thousands of disks spinning for hours, some of them will probably fail.

With our help, you will strengthen your team with experts, who know how to put Apache Hadoop to good use in your Big Data projects!

NoSQL databases

Such databases are usually simpler than common SQL databases. Besides, they are more efficient and scalable. Therefore, such technologies are better suited for storing and processing enormous amounts of data. Data Science UA will help you hire Big Data developers, who know how to use these tools efficiently.

There are various types of NoSQL datastores. For example, Riak, Redis, and Amazon DynamoDB transform data into collections of key-value pairs. On top of such structures, more complex models might come in handy. The main advantages of this approach are simplicity and speed. But it makes grouping common data complicated. Besides, you cannot define a sophisticated pattern on the database side.

Some other tools, as BigTable, Apache Cassandra, and Apache HBase, use a somewhat different scheme. Tables and the main columns describe the data. Such an approach is flexible, ensures fast writes, and can be scaled easily. However, performance parameters often require tuning and optimization.

Pig, Hive, and Impala.

Those frameworks are considered “the big 3” for data management in the Apache Hadoop ecosystem. We can find Big Data developers, who can skillfully use any of them.

Pig

Pig provides an ad-hoc way of creating and executing map-reduce operations on massive datasets. It was introduced in 2006 by Yahoo Research. The framework uses PigLatin language, and the queries have high fault-tolerance. You can extend Pig easily with user-defined functions;

Hive

Hive was introduced in 2007 by Facebook. It can warehouse petabytes of data. HiveQL (Hive Query Language) with SQL-like syntax is used. Unlike Pig, Hive is harder to extend with UDFs. The queries also have high fault-tolerance;

Impala

Impala was launched in 2012 by Cloudera. It utilizes an SQL query engine on HDFS for parallel processing and supports native (C++) and Hive user-defined functions. The query is aborted if a node fails.

Lambda Architecture

It is a practical approach to the real-time processing of enormous amounts of data. Lambda Architecture is based on the concept that the result is a function of input data (which is called lambda). The resulting solution performs as a lambda function, not linked to the volume of processed data.

The data is split into two parts:

• raw data – can’t be changed, only appended;
• pre-computed data subdivided into two categories: the “old” and the “recent” data depending on the operational context.

The three key building blocks of any system, based on the lambda architecture, include:

• Batch layer, which controls a set of raw data (master dataset) and pre-computes batch views — arbitrary query functions;
• Serving layer, indexing the batch views. As a result, ad-hoc queries with low latency can be performed;
• The speed layer accommodates all low latency requests—it deals with the recent data only.

If you want an engineer, capable of using this approach for the benefit of your business — contact us!

Reasons to hire Big Data developers in Ukraine:

A strong community of developers:

• 1,000+ software development providers
• 160,000+ software engineers
• Around 1,400 Big Data developers
• Numerous Big Data conferences, including international ones such as AI Ukraine Conference, AI & Big Data Day, JEEConf, etc.

Profound educational basis.

Ukraine has a strong STEM-based academic tradition. Ukrainian Catholic University offers a Master’s program in Computer Science with a specialization in Data Science. The students receive knowledge and skills in programming, data analytics, machine learning, artificial intelligence, Big Data system architecture, product development, etc.

High level of Big Data expertise.

Many businesses outsource Big Data developers to countries of Eastern Europe, including Ukraine. According to Clutch, Ukraine hosts the top 41 Big Data analytics companies. The list includes N-iX, CoreValue, DataRoot Lab, SoftServe, and more.

Several tips about the hiring process:

• We know almost all data engineers and developers and have a very friendly personal relationship with the majority of DS community members. So our advantage is a fast search of “warm” candidates.
  
• We’ll help you to hire Big Data engineers of any specialization and seniority level. Nonetheless, we are not limited to Data Science alone. We will gladly help you to find a developer in any other field – we successfully fill C-level executives, Admin, Backend, C/C++, QA/QA automation, Data Analysts roles.
• We coordinate our actions with the client, aiming for the optimal hiring process; Making cooperation effective and comfortable is among our main priorities;
• We look for prospective candidates and check their soft and hard skills. As a rule, we conduct one interview with a candidate, 2-3 interviews are on the client’s side;
• We close any position within 2-3 weeks. Usually, the candidate is ready to join your team in 2 weeks after getting an offer;

Why should you work with Data Science UA?

• Our recruitment is all about quality and speed. We hold our reputation dear and will do our best to hire Big Data engineers for you. We strive to build long-term relationships with different companies, finding loyal clients, partners, and even friends; 
• We are deeply integrated into the Ukrainian Data Science community. Data Science UA is involved in organizing numerous events and keeps in touch with top professionals in the field. Thus, we know where to find the best Big Data developers for hire in no time;
• We offer flexible options depending on the client’s needs, as result you get an optimal business model and costs minimization;

We will find a reliable partner for your Big Data endeavor, with a solid track record and strong expertise in such technologies as Hadoop, Spark, AWS, Cloudera, Pig, Hive, and more.